LET'S TALK

Custom Software vs SaaS: A Practical Guide to Making the Right Decision for Your Team

Pablo Etcheverry on October 11, 2025

Cover Image

Custom software vs SaaS: A practical build vs buy software guide for modern teams

Estimated reading time: 12 minutes

Key takeaways

  • Buy for commodity, standard problems where speed matters.
  • Build when workflows or customer experiences are differentiators and you need control over data, roadmap, or IP.
  • Hybrid often wins: use SaaS rails and build custom glue, dashboards, or microservices where latency, logic, or UX demand it.
  • Model TCO over 3–5 years and prioritize high ROI slices first.

Section 1 — Software leverage: why this decision matters

Choosing between custom software vs SaaS isn’t just a tech choice. It’s a decision about leverage and fit. The right call lets your team do more with less, move faster, and keep control where it matters. The wrong call slows growth and locks your workflows into someone else’s box.

Think of software leverage as a growth engine alongside labor and capital. People and money scale linearly. Code and media don’t. They’re permissionless leverage—you write code once, and it keeps working while you sleep.

“Code compounds output.” Naval’s leverage pyramid makes this concrete: labor and capital are linear; code and media are non-linear.

For most companies, leverage shows up in boring, beautiful ways: automation that trims hours into minutes, handoffs that go from days to clicks, dashboards that turn gut-feel into action. When the tool fits like a glove, the payoff is real.

Section 2 — Off-the-shelf software vs custom: definitions and context

Off-the-shelf / SaaS tools are built for the masses: sign up, configure, and get value fast. Vendors handle updates, hosting, and security. Great for standard jobs where best practices are known.

Custom software is built for your unique needs. It mirrors your processes, gives you control over features and data models, and evolves with your business.

Both exist for a reason. Generic tools optimize for breadth; custom optimizes for depth. When you’re connecting hardware and tooling—like pairing an ESP32 device with a dashboard that pushes OTA updates—the off-the-shelf approach often won’t cut it. See how Bench Sentry blended IoT, remote control, and tracking with a custom stack or how Kinetico built for industrial-grade telemetry.

Section 3 — Build vs buy software: a decision framework

Time-to-value is the first lens. Need outcomes in weeks? Buy. Can you invest months to shape a tailored outcome that compounds for years? Build.

Process uniqueness: If your workflows are a true differentiator, build. If the process is commodity, buy.

Integration complexity often pushes hybrid. Deep data orchestration and event-driven flows can break with superficial connectors; a focused custom layer can restore flow.

Control & roadmap: Need to own features and data model? Build. Ok with vendor roadmaps? Buy.

Budget & TCO—SaaS is cheaper up front but subscriptions and workarounds add up. Custom is front-loaded but can be cheaper over 5–10 years if it replaces many licenses.

Risk tolerance & team readiness: SaaS demands less maturity. Custom needs product leadership and an ops plan. A staged approach—start SaaS, add custom where it hurts—works well.

Rule of thumb: buy for commodity capabilities, build for differentiating workflows, and use hybrid for glue and extensions.

Section 4 — When to build custom software

Two primary reasons to build: 1) you’re creating a product to sell, or 2) you’re strengthening internal operations with bespoke internal tools.

Triggers — You outgrow generic tools, spend more time working around them than in them, face heavy manual exports/imports, or have compliance/data ownership needs vendors can’t meet.

Benefits: fit first, advantage next, and upside from owning IP. Examples:

  • Healthcare: Recovery Delivered needed a HIPAA-safe telemedicine flow—appointments, video, e‑prescriptions, records—so we built the platform to fit care delivery.
  • CRM: REE Medical unified personalized forms and workflows that generic CRMs couldn’t handle cleanly.
  • IoT: Bench Sentry paired devices over WiFi/Bluetooth and handled real-time events—classic build territory; see also Kinetico.
  • AI-driven UX: Mena Homes shows how tailored experiences around LLMs can be core to product value.

If these feel familiar—outgrowing SaaS, needing integration and data control—you’re likely in build mode.

Section 5 — When to choose SaaS (off-the-shelf)

SaaS shines for standard processes: email, payroll, HRIS, basic CRM, ticketing. You get speed, vendor support, and often better security posture than a small team can achieve day one.

Cost efficiency is real at early stages. Get live fast, learn from users, and avoid heavy upfront spend.

To avoid future constraints, choose tools with robust APIs, webhooks, and good export options. Favor configuration over heavy customization so you can extend later. For example, Mena Homes integrated OpenAI in a way that played nicely with their data.

SaaS vs custom is not binary: if the job is standard and speed matters, SaaS is your friend—pick vendors that won’t box you in later.

Section 6 — Hybrid strategies: the pragmatic middle

Most modern stacks are hybrid: SaaS for commodity functions plus a small custom layer for orchestration, automation, or unified UX.

Examples:

  • Hoober built an analytics hub that pulls listings, revenue, and leads into one dashboard with KPIs that make decisions obvious.
  • Payments: lean on Stripe for rails, build marketplace logic and KYC on top—MySide is a good model (MySide).
  • IoT + cloud: use cloud scale where it fits and a bespoke command center for control—see Bench Sentry and Kinetico.

iPaaS and low-code tools can accelerate the early glue work; graduate to microservices when scale or latency require it.

Section 7 — Economics and ROI: modeling the decision

Model the money before you write code. TCO is your first lens: subscriptions, integrations, storage, and hidden workaround costs for SaaS; discovery, build, testing, hosting, and maintenance for custom.

Measure returns: cycle time, error rate, throughput. If a task drops from 30 minutes to 5 minutes and runs 2,000 times a month, you’ve freed about 1,000 hours a year. Multiply by loaded hourly cost to quantify savings.

Use a simple payback model: build cost ÷ monthly savings = months to payback. Sensitivity test adoption to avoid rosy math.

Dashboards make impact visible. Pull from SaaS, a warehouse, or device telemetry. Hoober’s real-time dashboard is a useful pattern.

Don’t forget IP upside: owning proprietary software can lift exit multiples and reduce dependency risk. Examples: MySide and Flower Arranger show marketplace and payments patterns that protect long-term value.

Consider scale effects: SaaS often climbs with seats/usage; custom is front-loaded and may get cheaper per user as you grow.

Section 8 — Who should build it: in-house vs consultancy development

In-house gives deep domain fit and day-to-day control. Trade-off: time to hire and onboard, and carrying management load.

Consultancy brings speed and senior cross-functional teams on day one, plus battle-tested patterns. Trade-off: daily cost and the need for governance—protect IP and require documentation.

Many teams choose a hybrid: keep product ownership and SMEs inside, bring a partner to accelerate design and build, then pass the baton with docs and runbooks. That’s the model we favor at Imajine.

Regulated work benefits from experienced partners. Recovery Delivered compressed risk by using a team experienced in secure video, e‑scripts, and records.

Section 9 — Implementation roadmap for a successful custom build

  1. Discovery: map workflows, pain points, and edge cases. Sit with users and create a service blueprint.
  2. Prioritize by ROI: pick 2–3 high-value use cases with clear success metrics.
  3. Design architecture: integration map, data model, security plan, and decisions about SaaS vs custom. For device projects, plan cloud IoT and OTA updates; see Bench Sentry and Kinetico.
  4. Deliver iteratively: prototype, test with users, build in short cycles, use feature flags.
  5. Change management: simple guides, short videos, training sessions, and champions per team. For AR or on‑site tools, short demos help—see Glaziers Tools.
  6. Operate & evolve: monitoring, alerts, logging, shared KPIs, and a backlog for continuous improvement.

Section 10 — Common pitfalls and how to avoid them

  • Overbuilding version one: aim for the smallest slice that proves the outcome; validate with manual steps if possible.
  • Fuzzy requirements: appoint a product owner, write crisp stories with acceptance criteria, and triage scope weekly.
  • Underestimating integrations: test API limits, webhooks, and do dry runs for migrations.
  • UX debt: put real users in front of prototypes and fix the paper cuts early.
  • Ignoring maintenance: budget for upgrades, patches, and performance tuning.
  • Vendor lock-in: mitigate with standards, APIs, and exportable data.

Section 11 — Quick self-assessment checklist: custom software vs SaaS

Answer these to move from debate to a testable plan:

  • Is this capability core to how we win, or is it a commodity?
  • Are current tools slowing growth, quality, or compliance?
  • Do we need deep customization, integrations, or strict data control?
  • Do we have product leadership and budget to build and maintain?
  • Would owning this IP improve valuation or exit options?
  • Given the answers, is our decision Buy, Build, or Hybrid, and why?

Write down your call and the top three assumptions behind it. That converts a vague debate into a clear plan to test.

Conclusion and next steps

The right choice in custom software vs SaaS is about leverage, fit, and control. Buy where the job is standard and speed matters. Build where your process is your edge. Use hybrid to stitch it together with a calm, durable core.

Practical next steps:

  • Map current workflows.
  • Quantify the drag from today’s tools.
  • Model TCO and payback.
  • Run a small, high‑ROI pilot to prove the outcome before scaling.

If you want a second set of eyes, our team at Imajine is happy to help. We’ve shipped HIPAA‑compliant telemedicine, IoT dashboards with OTA updates, AI‑assisted search, AR visualizations, analytics hubs, and Stripe Connect marketplaces. Our initial consultation is free—share your goals and we’ll outline a Buy, Build, or Hybrid path.

FAQs

Is custom software always more expensive?

Not always over the full lifecycle. Custom costs more up front but can cost less over 3–5 years if it replaces multiple subscriptions, removes manual work, and lifts conversion. Biggest drivers are scope, integrations, security needs, and how often the product changes.

How long does a custom build take, and how do we de‑risk timelines?

Small, focused tools can ship in 6–12 weeks. Complex platforms can take several months. De‑risk with a tight MVP, short sprints, weekly demos, and feature flags. Ship value in slices, not one big bang.

Where do low‑code and no‑code tools fit?

Great for early validation, internal apps, and admin portals. Build a proof of concept fast, then harden the pieces that need scale or custom logic. Many teams keep a mix long term: low‑code for simple forms and dashboards, custom for core logic.

Can we start with SaaS and migrate later?

Yes. It’s a smart path. Choose tools with strong APIs and clean exports. Keep domain logic in a thin custom layer where possible so you can swap SaaS parts or replace them with custom services without breaking users.

How do we protect IP and ensure knowledge transfer when using a consultancy?

Set IP terms in the contract. Require code in your repos, detailed documentation, architecture diagrams, and runbooks. Ask for a formal handover and joint on‑call for the first weeks. Pair your engineers with the partner during the build so context stays in‑house.

How do we measure ROI after launch?

Track baseline metrics before you start. After launch, watch cycle time, error rate, support tickets, NPS, and revenue or margin changes. Use an analytics dashboard so everyone sees progress. Hoober’s KPI model is a good reference for visibility.

What about hardware‑software projects in IoT?

Plan the full stack: firmware, connectivity, cloud, and apps. Use proven boards like ESP32 for Bluetooth and WiFi, and build a web dashboard for alerts and OTA updates. Bench Sentry and Kinetico show the pattern end to end.

Model Context Protocol (MCP): Connect ChatGPT Seamlessly to Google Calendar, Sheets, Slack, and More

Pablo Etcheverry on October 11, 2025

Cover Image

Model Context Protocol (MCP): The simplest way to connect ChatGPT to Google Calendar, Sheets, Slack, and Blender

Estimated reading time: 8 minutes

Key takeaways

  • MCP is a single, standard bridge that lets an LLM orchestrate external tools with natural language.
  • Provider-backed servers mean you configure once and avoid bespoke connector maintenance.
  • Workflows can chain across Calendar, Sheets, Slack, and even local tools like Blender.
  • Safety relies on least-privilege scopes, service accounts, and dry-run previews before commits.

What is MCP? MCP explained

Model Context Protocol is a simple standard that lets an AI assistant “talk” to external apps through MCP servers. You describe the outcome you want. The LLM turns your words into tool calls. The MCP servers (run by providers like Google and Slack) do the work and send results back.

The core idea is straightforward: connect an LLM to external tools without writing custom code for every single app. One protocol. Many tools. Natural language on top.

Why it’s trending: the hard parts are finally standardized and maintained by providers. Authenticate once. Approve scopes once. Then orchestrate Calendar, Sheets, Slack, and more with the same approach.

Compared to plugins or one-off integrations, MCP gives you:

  • One protocol instead of many bespoke connectors.
  • Provider-managed servers instead of DIY maintenance.
  • A unified permissions model you can reason about and audit.

How Model Context Protocol (MCP) works (under the hood, but simple)

Components

There are three main pieces:

  • The LLM client (for example, ChatGPT) where you type your request.
  • MCP servers provided by each service (Google for Calendar/Drive, Slack for messaging).
  • A shared message format the LLM uses to call those tools — under the hood it’s JSON-RPC 2.0 over standard transports (STDIO for local tools, HTTP for remote ones).

Workflow

You ask in natural language. The LLM converts intent into MCP calls. The MCP servers execute the operations—read the calendar, update the sheet, post in Slack—and return structured results. The LLM reads the results, reasons about next steps, and can chain more calls. One prompt can fan out across multiple tools, then converge back into a single, clean update for you.

Key advantages

You configure connections once, providers maintain them, and the assistant orchestrates across apps in one go. If you’ve ever thought, “I just wish ChatGPT could do the thing in my actual tools,” this is that wish, formalized.

Real-world scenario (Pepe’s handoff)

Meet Pepe, a project coordinator. His old routine took ~45 minutes: scan Google Calendars, update a Google Sheets tracker, post meeting details in Slack, and monitor replies. With MCP + ChatGPT, Pepe types one prompt and the LLM checks calendars, updates Sheets, and posts in Slack — all in under a minute. The invites are correct, the sheet is fresh, and the channel gets a tidy summary.

At Imajine, we see this pattern across teams every day. It’s why we build dashboards that show state at a glance, like on Hoober. MCP extends that clarity into action: your LLM not only reports status—it updates it.

MCP tutorial — How to use MCP with ChatGPT

Prerequisites

You need an LLM client that supports MCP (e.g., ChatGPT) and accounts for the tools you want to connect: Google Calendar, Google Sheets, Slack. Ensure you or your admin has the right permissions. If you plan to add Blender later, confirm local access to scenes and assets.

Configuration basics

Open your LLM client’s connector settings and authenticate to each provider’s MCP server. It feels like a normal OAuth sign-in. Approve only the scopes you need (read/write events for Calendar, read/write for Sheets, message posting for Slack). Providers maintain the server — you don’t write code or babysit tokens day to day.

First-run checklist

  • Tell the LLM which calendars to check and the timezone to use.
  • Specify the sheet and tab for your tracker and the meaning of each column.
  • Identify the Slack channel for updates and whether posts should be threaded or pinned.

Example natural-language prompts

  • “Find a 30-minute slot tomorrow morning when the engineering team is available and schedule a ‘handoff review.’”
  • “Update the project tracker in Google Sheets with completed tasks from the last 24 hours and summarize progress.”
  • “Post an urgent meeting reminder in Slack with the sheet link and ask for confirmations.”

If you already ship LLM tools and want a head start, check how we approached LLM-led workflows on Mena Homes. The same natural-language patterns carry over to MCP orchestration.

A quick note on trust and safety: run a dry run. Ask, “Show me what you plan to change before you commit.” The LLM will preview event details, ranges in Sheets, and the Slack message. Confirm, then let it execute.

From here, move into specific playbooks. In the next sections we’ll cover integrations with Google Calendar & Sheets, Slack, Blender, and advanced developer flows.

Integration specifics

MCP integration with Google Calendar and Sheets

Calendar gets smarter when the LLM can read and write your schedule. With MCP you can scan multiple calendars for overlapping availability, create events with Meet links, invite attendees, reschedule, or cancel from one prompt. Ask for constraints like time zones, working hours, or room resources, and the MCP server will return valid options.

Sheets works the same way: fetch rows by filters, append entries, update statuses, and pull computed values from formula cells. Good patterns:

  • Name tabs clearly and lock down ranges you expect to touch.
  • Ask the assistant to show the rows it will change before committing.
  • Wire a summary step: “Compute percent complete and return it as a KPI.”

We use this approach on tools like Hoober to surface KPIs where work happens, not in a separate tool.

MCP Slack integration

Slack becomes a broadcast and coordination layer. With MCP the assistant can post announcements, reply in threads, pin messages, or DM owners who missed updates. Best practices:

  • Create a test channel first, then invite the bot to production channels where automation is allowed.
  • Use threads for rollups: a single post with a tidy thread for follow-ups.
  • Mention stakeholders by handle so they can confirm.

If you need a blueprint for channel hygiene with analytics, see the Mena Homes dashboard pattern where summaries and KPIs keep people aligned without spam.

MCP Blender integration

MCP can drive local tools like Blender. The assistant can open a scene, change materials, tweak object positions, render stills or animations, and export assets. Example prompt: “Open product-template.blend, swap the material to our five brand colors, render at 1080p with the studio camera, and save to /assets/variants.”

Always ask for a dry run report listing file path, camera, samples, and output size before rendering.

Advanced workflows — MCP with Cursor and Python

Cursor brings MCP into your editor so you can chain steps without leaving code. Treat each tool call as a check: verify the Calendar slot, validate the Sheets result, then proceed. This gating pattern makes workflows predictable.

Python adds scheduling and storage. Example: a cron job checks logs hourly, writes anomalies to a “Production Incidents” sheet, creates a Calendar event for on-call, and posts a Slack alert with a chart link. Add idempotency by comparing hashes and retries with backoff for robustness.

For physical-world connections, extend MCP to local servers that talk to devices (we’ve done this with ESP32, Bluetooth, and WiFi on Bench Sentry and Kinetico Pro). The pattern is the same: MCP client calls a local server, the server talks to hardware, and returns a clean result for the LLM to reason about.

Security, privacy, and governance

Principles:

  • Grant least privilege — use calendar.readonly unless write is necessary.
  • Use dedicated service accounts for automations, not personal logins.
  • Keep version history and audit logs enabled for Sheets, Calendar, and Slack.
  • Enforce SSO and rotate tokens on a schedule for enterprise rollouts.

Separate identities and role-based access make audits and offboarding safe. The MCP server executes actions with approved scopes and returns only the data needed for the assistant to reason and respond.

Who should use Model Context Protocol (MCP) and when

MCP helps people who repeat multi-app work:

  • Project coordinators
  • Product managers
  • Support leads
  • Marketing teams that render variants and schedule launches
  • Solo creators who want a light studio assistant

It shines when steps are known but details change: weekly standups, monthly reporting, sprint demos, campaign checklists, and status aggregation. If you manage assets, MCP can churn through renders while you focus on creative choices.

Alternatives and comparisons

Traditional APIs: give full control but cost time and maintenance. MCP trades low-level control for speed and low upkeep.

No-code automations (Zap-like): good for simple triggers but limited in flexible reasoning. MCP + ChatGPT can infer and choose the best action before acting.

Success metrics and rollout plan

Measure:

  • Time saved per run (e.g., 45 minutes → 1 minute).
  • Error rates (missed invites, stale statuses).
  • Data freshness (average age of Last Updated).

Rollout plan:

  1. Start small: pick one high-friction workflow and document the manual path.
  2. Build the MCP version and run both for two weeks.
  3. Capture working prompts as templates and add guardrails like “preview before commit.”
  4. When stable, expand to the next workflow and add advanced integrations (Blender, CRM, IoT) later.

For CRM-heavy teams, our REE Medical case study shows how to unify fragmented data and personalized forms; the same discipline helps when you bring MCP into customer ops.

FAQ

Do I have to maintain the connections myself?

No. Providers maintain their MCP servers. You authenticate once, approve scopes, and you’re set. You may re-auth when tokens expire, but you don’t host or patch the servers.

Why am I seeing permission errors?

Most likely your scopes don’t cover the action. calendar.readonly can’t create events. A Slack bot without channel access can’t post. Edit the connection, add needed scopes, and invite the bot to the right channels.

What if APIs rate limit me?

Batch changes and space calls out. Queue Slack posts. For Sheets, group row updates by range rather than single-cell writes. If volume is high, spread runs across time windows.

The sheet update failed with a range error. What now?

Use exact sheet, tab, and A1 ranges. Names like “Q1 tracker” vs “2025-Q1-Projects” cause misses. Keep a canonical reference doc of IDs for calendars, sheets, and channels. Have the assistant read the first five rows to validate before writing.

Can MCP work offline or with flaky internet?

Local tools can use STDIO, so you can operate against Blender or a local script offline. For cloud tools, queue actions. Ask for explicit success confirmations and retry on reconnect.

How is this different from plugins?

Plugins are bespoke to one app. MCP is one protocol many tools share. It uses a standard message format and provider-run servers, so you get a single mental model for permissions, calls, and logs.

Can I run a private MCP server?

Yes — useful for local tools or internal systems. Expose specific functions, handle auth on your side, and the assistant calls your server like any other. This is common for on-prem or regulated data.

Is MCP safe for enterprise?

Treat it like any integration: least-privilege scopes, SSO, token rotation, sandbox testing, and audit logs. Separate service accounts from human users. With these basics, MCP can meet enterprise needs.

Can MCP control IoT devices like ESP32?

Yes, through a local or remote MCP server that talks to your hardware libraries over Bluetooth or WiFi. See Bench Sentry for remote control and package tracking, and Kinetico Pro for commercial sensor data at scale.

Does Blender need to stay open during renders?

If the MCP server launches Blender headless, it will manage the process for you. If you attach to a running instance, keep it open until jobs finish. Always validate file paths and render settings in a dry run first.

How do I audit changes?

Rely on native logs: Google Sheets version history and Calendar change logs show who changed what and when. Slack audit logs track bot messages. Keep MCP request/response logs when you need deeper forensics.

Conclusion

Model Context Protocol (MCP) turns natural-language instructions into coordinated actions across Calendar, Sheets, Slack, and even Blender. Describe the goal. The assistant reasons, calls the right tools, and reports back with results you can trust.

If you want a fast win, pick one workflow, run the MCP tutorial steps, and ship your first end-to-end prompt in ChatGPT. When you’re ready for advanced prompts in Cursor and Python, analytics dashboards, or IoT control, we can help. Imajine has shipped AI/ML products like Mena Homes, dashboards like Hoober, AR visual tools like Glaziers Tools, and IoT platforms using ESP32, Bluetooth, and WiFi such as Bench Sentry and Kinetico Pro.

Our initial consultation is free — tell us your workflow and we’ll help design a safe, clear rollout for MCP that saves hours every week.

HIPAA Compliant GPT: How to Set Up Using AWS Bedrock, Google Vertex AI, and Azure OpenAI

Pablo Etcheverry on October 11, 2025

Estimated reading time: 10 minutes

Key takeaways

  • You can run a HIPAA compliant GPT today if you use cloud providers that sign a Business Associate Agreement (BAA).
  • Top HIPAA-friendly platforms: AWS Bedrock, Google Vertex AI, and Azure OpenAI—each offers enterprise controls and data-use guarantees.
  • Pricing is often comparable to direct vendor rates; expect small extra costs for networking, logging, and fine-tuning hosting.
  • Follow a practical checklist: BAA, private networking, encryption (CMEK/KMS), strict IAM, audit logging, and PHI minimization.

Opening (hook + promise)

HIPAA compliant GPT does not require you to avoid GPT, Claude, or Gemini. You can run a HIPAA compliant GPT today.

Here’s the key: use cloud providers that sign a Business Associate Agreement (BAA) and offer enterprise-grade controls. That’s how you protect PHI, keep audit trails, and ensure your data isn’t used to train public models.

In this guide you’ll get:

  • Which providers to use — AWS Bedrock, Google Vertex AI, Azure OpenAI
  • Model options — Claude, GPT‑4, Gemini — and HIPAA-compliant AI posture
  • Real pricing realities
  • A practical setup checklist you can follow this week

Keep scrolling for the exact steps and tradeoffs that matter in the real world.

HIPAA basics for AI usage

HIPAA focuses on PHI data protection. For AI, that means:

  • Safeguards: encryption, access controls, and breach response
  • Data handling: limit who sees PHI and why; keep audit logs
  • Accountability: prove what happened, when, and by whom

Why a Business Associate Agreement (BAA) matters:

  • A BAA binds the provider to HIPAA rules
  • It enforces proper PHI handling and breach duties
  • It is the contract layer that makes HIPAA compliant LLMs possible at scale

Helpful context on HIPAA security expectations:

The three main HIPAA-friendly routes to top models

AWS Bedrock (HIPAA)

What you can use:

  • Anthropic Claude (e.g., Sonnet, Opus)
  • Meta Llama, Amazon Titan, and more

Why teams choose it:

Where it shines: Fast access to the newest Claude models and strong PHI data protection controls out of the box.

Google Vertex AI (HIPAA)

What you can use: Gemini (Pro, Flash), select PaLM, and open-source models.

Why teams choose it:

Where it shines: Gemini for fast, cost-effective reasoning and tight integration with Google Cloud security.

Azure OpenAI (HIPAA)

What you can use: GPT‑4 family, GPT‑4 Turbo, DALL·E, and more.

Why teams choose it:

Where it shines: Organizations standardized on Microsoft security and easy policy enforcement with Azure Policy and logging.

Pricing reality check (cost is comparable to going direct)

Good news: HIPAA compliant GPT does not have to be pricey. In many cases, you’ll pay similar rates to going direct.

What we see in the field:

Extra costs to watch:

  • Fine-tuned model hosting and training fees (watch Azure OpenAI hosting costs: Azure pricing).
  • Egress/networking, logging, and key management across clouds.

Takeaway: With a BAA and enterprise controls, HIPAA compliant AI can be cost-parity with direct vendor APIs—without sacrificing PHI data protection.

Implementation experience and setup flow

If you’ve built on OpenAI/Anthropic/Google APIs, building on Bedrock, Vertex AI, or Azure OpenAI will feel familiar. The main difference is extra guardrails: auth, network, and logging.

What changes:

  • Auth and identity: use IAM (AWS), IAM (GCP), or Entra ID/RBAC (Azure)
  • Networking: private endpoints/VPC/VNet to keep traffic off the public internet
  • Logging and keys: centralized audit logs and KMS/CMEK everywhere

Practical setup checklist

  • Choose your provider(s) based on your primary models (Claude → AWS Bedrock, GPT‑4 → Azure OpenAI, Gemini → Vertex AI).
  • Execute a Business Associate Agreement (HIPAA BAA for AI) with your cloud provider.
  • Configure dedicated enterprise infrastructure:
  • Lock down data-use settings:
  • Implement PHI minimization/redaction:
    • Drop identifiers you don’t need (name, MRN, SSN).
    • Use pattern-based redaction or de-identification before prompts.
    • Re-identify only on the client or secure service layer.
  • Enforce least privilege and secret hygiene: fine-grained IAM, rotate keys, store secrets in KMS/Key Vault/Secret Manager.
  • Document everything for audits: data flows, subprocessors, retention policy, access reviews, incident response, and model cards/use cases.

Tip: Think in layers: network isolation, encryption, identity, logging, and data-use controls. Each layer blocks a different risk. Together, they create robust enterprise AI security.

Access and approval timelines (what to expect)

Access isn’t hard, but timing varies by provider and account history.

What teams report:

  • AWS Bedrock: often immediate once the service is enabled in your account/region.
  • Google Vertex AI: usually available right away; some orgs see 1–2 business days for quota increases.
  • Azure OpenAI: access requires approval; typical is ~1 business day, sometimes longer based on use case.

If you need day-one access to brand-new models, there are tradeoffs and workarounds. In the next sections we cover model availability timing, a medical transcription case study, and a quick-start guide you can run this week.

Tradeoffs vs. going direct to model vendors

Model availability timing

  • New models don’t always land everywhere at once.
  • AWS Bedrock often gets new Claude releases quickly; Gemini updates land in Vertex AI first; GPT‑4 family updates arrive in Azure OpenAI after OpenAI.com.
  • Expect a lag from a few days to several weeks depending on provider and region.

When day-one access matters

If you need immediate access for research or feature testing, going direct to a model vendor may be faster — but direct APIs usually don’t include a BAA or full enterprise controls you need for PHI protection.

For production with PHI, the safer path is AWS Bedrock HIPAA, Google Vertex AI HIPAA, or Azure OpenAI HIPAA with a signed BAA and private networking.

Mitigations: get the best of both

  • Run a multi-provider strategy: prototype on whichever service has the newest model, then move to your HIPAA-compliant stack before real PHI traffic.
  • Keep a portable prompt and schema: use a consistent JSON output spec across providers.
  • Build a thin adapter layer: one interface, many backends (Bedrock, Vertex, Azure).
  • Lock in controls, not vendors: make network, IAM, logging, and DLP the foundation so you can swap models without reopening compliance work.

Real-world case study: HIPAA-compliant medical transcription app

Context

A multi-site medical group wanted fast, accurate clinical notes from visit audio. Strict PHI rules, detailed audit logs, and no training on customer data. Goals: clean transcripts, smart editing, and safe clinician chat.

Architecture choices

  • Speech-to-text: existing ASR vendor output sent into secure cloud storage.
  • Transcript cleanup and structure: Claude via AWS Bedrock for sectioning, grammar, and SOAP note formatting.
  • Chat-based editing and Q&A: Gemini via Google Vertex AI for quick follow-ups and formatting tweaks.
  • Why these picks: Claude quality on Bedrock and Gemini low-latency chat on Vertex (Bedrock data privacy, Vertex data governance).

Data flow (PHI-aware)

  1. Upload audio and ASR text to a private bucket with CMEK/KMS encryption.
  2. Run de-identification on obvious identifiers before LLM calls when possible.
  3. Send batched, minimized text to Claude on Bedrock via PrivateLink.
  4. Store LLM outputs with audit logs (CloudTrail/CloudWatch or Cloud Logging).
  5. Provide an editor UI where staff ask Gemini for changes.
  6. Re-identify only at the secure service layer, then export to EHR.

Security and governance

  • Private networking end to end: AWS PrivateLink and Google Private Service Connect/VPC Service Controls (AWS PrivateLink, Google VPC SC).
  • Keys in KMS/CMEK; strict IAM/RBAC roles; secrets in Key Vault/Secret Manager equivalents.
  • Model data-use controls disabled by default; no training on customer data (Bedrock data privacy, Vertex governance).

Outcome

  • Clinicians received cleaner drafts in seconds, with fewer edits.
  • PHI stayed in HIPAA-eligible services under a Business Associate Agreement.
  • Cost was near vendor direct rates, plus small spend for networking and logs.
  • The team kept the option to add Azure OpenAI later for GPT‑4 features while keeping Azure OpenAI HIPAA guardrails (Azure data privacy).

Advanced options and extensibility

Host or customize models

  • Bedrock supports multiple foundation models and enterprise controls; check HIPAA eligibility for any new capability before using PHI (AWS HIPAA reference).
  • Vertex AI supports tuning and grounding with enterprise governance; align scopes with VPC Service Controls and DLP (Vertex governance).
  • Azure OpenAI supports fine-tuning and model deployments with private networking and Key Vault integration (Azure private networking).

Fine-tuning within HIPAA constraints

  • Use de-identified datasets for training when possible.
  • Keep raw PHI in your VPC/VNet and apply strict access controls.
  • Budget for fine-tune hosting and training costs, especially on Azure OpenAI (Azure pricing).

Observability and governance add‑ons

  • Centralize logs: CloudTrail/CloudWatch, Cloud Logging, Azure Monitor.
  • Add DLP and redaction at ingress and egress.
  • Human review queues for sensitive outputs (e.g., discharge notes).
  • Regular access reviews and incident runbooks to back your HIPAA compliant AI controls (HIPAA security guidance).

Quick-start guide: Make your GPT deployment HIPAA-compliant

  • Decide your workloads: transcription cleanup, SOAP notes, patient summaries, chat, coding suggestions.
  • Pick your models: Claude for structured clinical writing; GPT‑4 on Azure for broad reasoning; Gemini for fast chat.
  • Choose providers: AWS Bedrock HIPAA for Claude; Google Vertex AI HIPAA for Gemini; Azure OpenAI HIPAA for GPT‑4.
  • Execute your HIPAA BAA for AI: Ensure the services you’ll use are in scope under the BAA (AWS, Google, Microsoft).
  • Set up enterprise AI security: Private endpoints (PrivateLink, Private Service Connect/VPC SC, Azure Private Link), TLS and KMS/CMEK, and audit every call.
  • Lock down data-use: Confirm prompts and completions aren’t used to train models (AWS, Google, Azure).
  • Minimize PHI: Redact unnecessary identifiers; re-identify only inside your secure app.
  • Pilot and scale: Validate latency, cost, and quality; add rate limits, retries, and circuit breakers; document data flows and retention for audits.

FAQ

Are GPT or Claude HIPAA compliant by default?

No. The models themselves are not “HIPAA compliant” on their own. Compliance comes from how you deploy them: under a BAA, with enterprise controls, and with safeguards around PHI. Using HIPAA-eligible services like Bedrock, Vertex AI, or Azure OpenAI is the usual path.

Do OpenAI or Anthropic sign BAAs via standard APIs?

Most teams do not rely on direct vendor APIs for PHI because a BAA and enterprise controls are not typically available in standard self-serve plans. Instead, teams use cloud providers that sign a BAA and provide network isolation, IAM, and audit logging.

Will my PHI be used to train models?

On HIPAA-eligible cloud services, providers state that prompts and completions are not used to train foundation models. Always verify and disable any data retention features (AWS, Google, Azure).

Is running local LLMs safer than cloud?

It can be, but only if you match enterprise AI security: physical security, encryption, RBAC, patching, high availability, monitoring, and incident response. For most teams, HIPAA-eligible cloud services with a BAA are faster and safer to operate at scale (HIPAA security guidance).

What’s the cost difference between HIPAA compliant LLMs and direct APIs?

Often small to none. Azure OpenAI typically aligns with OpenAI pricing; Bedrock pricing for Anthropic models is similar to Anthropic direct; Vertex AI is close to Google’s public rates. Expect extra spend for networking, logging, and fine-tuned model hosting (Azure pricing, OpenAI pricing, Bedrock pricing, Anthropic pricing, Vertex pricing).

Can I use multiple cloud providers at once?

Yes. Many teams mix AWS Bedrock for Claude, Vertex AI for Gemini, and Azure OpenAI for GPT‑4. Build a small abstraction layer and keep prompts portable to avoid lock-in.

How long does it take to get access?

  • Bedrock: often immediate after enabling the service (getting started).
  • Vertex AI: usually immediate; quotas may take 1–2 business days (quotas).
  • Azure OpenAI: approval is required; many teams see about one business day (Azure OpenAI access).

What controls matter most for PHI data protection?

Private networking, encryption with CMEK/KMS, strict IAM/RBAC, audit logs, and clear data-use settings that prevent training on your data. Add DLP and PHI minimization for defense in depth (HIPAA guidance).

Conclusion and next steps

You can ship HIPAA compliant GPT today. Use HIPAA-eligible services with a signed Business Associate Agreement, then layer network isolation, encryption, IAM, logging, and data-use controls. AWS Bedrock, Google Vertex AI, and Azure OpenAI give you top models—Claude, Gemini, and GPT‑4—without sacrificing PHI data protection.

A smart path: start where your must-have model lives, keep prompts portable, move production PHI to the cloud that gives you the BAA and controls you need, and revisit your mix as models and prices change.

If you want help standing this up, grab our checklist, subscribe for practical updates, or reach out. We’ll get your first HIPAA compliant AI workflow live this week—and your HIPAA compliant GPT stack ready for scale.

2026 technology trends: Key breakthroughs transforming work, AI, and daily life

Pablo Etcheverry on September 15, 2025

Cover Image

2026 technology trends: 17 breakthroughs reshaping work and everyday life

Estimated reading time: 10–12 minutes

Key takeaways

Quick summary — what to remember:

  • Automation moves from tasks to end-to-end processes; people become supervisors of smart systems.
  • Embodiment: commercial robots and humanoids are entering repeatable roles in logistics and manufacturing.
  • New interfaces: AR glasses, wearables, and BCI bring computing closer to senses.
  • Compute shifts to on-device and edge AI for speed, privacy, and lower cost.
  • Content workflows flip to generative-first pipelines and require provenance and governance.

Notable callouts: Agents, on-device AI, and robots are the three forces that will reshape workflows in 2026.

Table of contents

  1. Overview
  2. The future of work automation
  3. Low-code / no-code builders
  4. On-device AI & edge chips
  5. Intelligent physical world
  6. New interfaces (XR, wearables, BCI)
  7. Sector breakthroughs (healthcare, quantum)
  8. Generative content pipelines
  9. Privacy, trust & security
  10. What this means for teams
  11. FAQ

Overview

In this overview of 2026 technology trends, we unpack how AI could automate up to 70% of everyday tasks by 2026 and spotlight 17 AI trends watchers should know—from AI agents and automation to humanoid robots, AR glasses and extended reality, on-device AI and edge AI chips, and brain-computer interfaces (BCI).
We’ll show real examples, why they matter for work, and how they show up in daily life. Keep this open as your simple map for an AI-native year ahead.

Why these 2026 technology trends matter now

  • Automation shifts from tasks to whole processes — many roles redesign around supervising smart systems, not executing every step.
  • Convergence: AI moves on-device, robots leave labs for warehouses and stores, and interfaces jump from screens to space (XR) and even mind (BCI).
  • Content flips: up to 90% of online content could be AI-generated by 2026, changing how teams create, review, and govern work.

How to read this guide

  • Work: AI agents, workflow automation, and AI-native OS.
  • Build: low-code no-code development platforms.
  • Compute: on-device AI and edge AI chips.
  • Physical world: robots, smart cities, and homes.
  • Next up (Part 2): new interfaces (XR, wearables, BCI), sector breakthroughs, genAI content, and trust.

Ready to see how the future of work automation shows up today? Scroll on.

The future of work automation: AI agents and automation become everyday teammates

AI agents and automation take on projects, not just prompts

What it is: Autonomous agents plan tasks, use tools, execute steps, and iterate. You state the goal; they do the grind.

Examples you can picture:

  • A coding agent like Devin builds and deploys a small web app end to end.
  • An Auto‑GPT–style travel agent books a multi‑city trip within a budget.
  • An internal onboarding agent gathers docs, provisions accounts, and answers FAQs for new hires.

Why it matters:

  • Roles shift from “doers” to “directors.” You set specs, review outputs, and handle edge cases.
  • Teams need guardrails: access controls, logs, and clear escalation — see guidance on agent governance.

Workflow automation at scale

Think end‑to‑end, not piecemeal. Platforms tie many steps together.

  • In the wild: ServiceNow, UiPath, and Zapier cut repetitive work by up to 65% when applied across a process, not just one task.
  • Amazon‑style predictive orchestration routes packages, picks stock, and schedules shifts based on live signals.

Impact you can measure:

  • Faster cycle times, fewer errors, and lower costs.
  • Better employee experience: less swivel‑chair work; more customer time.

AI-native operating systems

Your OS becomes your co‑pilot.

  • Microsoft Copilot in Windows 11 can summarize files, rewrite emails, and generate images at the system level—no app shuffle.
  • Apple threads AI deeper into macOS/iOS with on‑device models that respect privacy and speed — learn more about Apple’s approach.

Why it matters: Less time switching apps, more time in flow; new governance for model choice, data boundaries, and auditing.

Build without barriers: low-code no-code development platforms go mainstream

What it is: Drag‑and‑drop app builders let non‑engineers ship apps, automations, and mini‑systems.

  • Tools to know: Bubble, Glide, Microsoft Power Apps, and Google AppSheet.
  • Real examples: A field team spins up a parts‑tracking app in a week; ops leaders wire a custom GPT to draft SOPs from policy docs—no code.
  • Why it explodes now: Demand beats dev capacity. Low/no code closes the gap fast. Even domains like agricultural IoT surge, with market size projected at $18.7B by 2026.

Impact: By 2026, many new internal apps may be low/no code—but IT must set guardrails (data access, change control, review paths).

AI beyond the cloud: privacy-first, on-device AI and edge AI chips

Privacy-first AI and on-device processing

The center of gravity shifts from cloud to edge.

  • Apple’s Neural Engine runs AI locally for speed and privacy.
  • Meta’s models, like Llama 3 variants, are optimized to run on‑device for chat and vision tasks.
  • Intel’s Meteor Lake chips add NPUs to handle AI without draining battery.
  • Regulations such as GDPR and CCPA push companies to process and store less data in the cloud.

What you feel: snappier experiences, fewer privacy pop‑ups, and apps that still work when the network is shaky.

On-device AI and edge AI chips everywhere

  • Flagship silicon: Apple A17 Pro/M4, Qualcomm Snapdragon X Elite, and Intel Meteor Lake bring big on‑device gains. See demos and commentary at recent briefings.
  • New moments include real‑time translation during a call, instant voice commands offline, and one‑tap photo cleanup with near‑zero lag.
  • Business impact: lower cloud bills, better reliability, and simpler compliance when data stays local.

The intelligent physical world: robotics, logistics, homes, and cities

Smart infrastructure and IoT 2.0

The world is dotted with billions of connected devices — pipes that enable new services. Examples and pilots are emerging rapidly (see deployments).

AI-enhanced robotics in retail and logistics

  • Agility Robotics’ Digit pilots with Amazon for tote moving and sorting.
  • Walmart‑style shelf scanners check price tags and stock; Starship and Kiwibot deliver with AI vision and mapping.
  • Impact: higher throughput, safer jobs, and rising human‑robot teamwork.

AI-powered home assistants evolve

  • Amazon Astro patrols, checks on loved ones, and links to Alexa skills.
  • Apple explores a tabletop robot that gestures and displays info for natural help.

Humanoid robots 2026 go commercial

  • Figure AI partners with BMW for manufacturing tasks; Tesla Optimus works on factory routines; Digit handles logistics.
  • Why now: costs trend toward small‑car pricing and safety/autonomy reach usable levels for controlled sites.
  • Plan pilots in narrow, repeatable roles and train people on safe co‑work and incident playbooks.

We’ve seen how work changes, how anyone can build, how AI runs on‑device, and how the physical world gets smart. Next up: new interfaces, sector breakthroughs, creative workflows, and trust. Learn more.

New interfaces: AR glasses and extended reality, wearables, and brain-computer interfaces (BCI)

AR glasses and extended reality

Lightweight glasses are getting useful. They layer captions, arrows, and tips over the world.

  • Apple Vision Pro momentum pushes devs to build serious XR apps.
  • Meta, Xreal, and Samsung work on glasses for hands‑free info.
  • AI makes XR dynamic—NVIDIA‑style characters can chat and adapt in real time. Virtual stores change layouts as you move.

Why it matters: Less “pull” on your phone; more “push” at a glance—translation, directions, and context right where you look. As networks improve, expect more immersive time: up to an estimated 25% of users could spend an hour a day in metaverse‑style spaces by 2026.

Wearables that know you better

Wearables shift from steps to health signals that help you act earlier.

  • Oura and Whoop track sleep, recovery, stress, and skin temperature to give early illness hints (research & demos).
  • R&D includes non‑invasive glucose and wrist blood pressure prototypes.

The rise of brain-computer interfaces (BCI)

  • BCI turns neural intent into actions — starting as assistive tech.
  • Real progress: Neuralink’s implant showed a thought‑controlled cursor; Synchron and Precision Neuroscience work on less‑invasive systems.
  • Why it matters: accessibility gains for messaging, mobility, and independence; long-term, BCI could add a new interface layer alongside voice and touch.

Sector breakthroughs to watch: healthcare and quantum

AI in healthcare gets personal

  • DeepMind research shows retinal scans can flag 21 diseases—fast, non‑invasive screening.
  • Models that alert on sepsis or cardiac risk hours earlier help clinicians act sooner.
  • Oncology teams tailor chemo using genetic profiles to boost outcomes.

Why it matters: earlier detection, fewer false alarms, and less paperwork burden. VR in healthcare could reach $40.98B by 2026 for training, therapy, and pain management.

Quantum computing progress 2026

Quantum won’t replace classical computing—but pilots are getting real.

  • State of play: IBM targets 1,000+ qubits with roadmaps; Google, IonQ, and Rigetti push hardware and stacks.
  • Early use cases: molecular simulation for drug discovery and combinatorial optimization for supply chains.
  • Caveat: near‑term value comes from hybrid quantum/classical workflows focused on narrow problems.

Content creation reimagined: generative AI becomes the default

GenAI now spans text, image, audio, and video in one flow.

  • Teams use GPT‑5/Gemini Ultra‑class models for research and drafting, Adobe Firefly and Runway for images/video, and ElevenLabs for voice.
  • New pipelines: Brief → storyboard → AI draft → human edit → QA → publish, with provenance checks built in.
  • Expect up to 90% of online content to be AI‑generated by 2026, raising the bar for review, IP checks, and brand‑voice standards.

Trust tools: watermarks, C2PA‑style metadata, and model cards help manage risk and keep quality high.

Smart infrastructure for privacy and trust

Governance and compliance

As on‑device AI scales, leaders need clear rules. Set guardrails: model selection based on risk tier, data residency, consent capture, retention by default, and full audit trails for prompts and actions (example guidance).

Security for IoT 2.0 and robots

  • Practical steps: zero‑trust networks for billions of devices — no implicit trust.
  • SBOMs for all smart devices; patch SLAs; continuous monitoring.
  • Physical fail‑safes: e‑stops, safe modes, and fenced zones for mobile and humanoid robots.

Ethical design

  • Bias testing in healthcare AI across genders, ages, and ethnicities.
  • Transparent logs for AI agents so humans can override decisions.
  • Clear UX for consent and data use—plain words, not legalese.

What this means for teams and individuals

For business leaders

  • Pick 2–3 end‑to‑end processes for automation pilots with a 90‑day ROI target.
  • Compare AI‑native OS features across vendors; standardize where it boosts productivity.
  • Plan an edge inference roadmap to cut cloud cost and latency (see recommendations).

For product and IT

  • Expand low‑code platforms with guardrails: data access tiers, review queues, version control.
  • Stand up fleet management for edge devices, wearables, and robots—deploy, monitor, roll back.
  • Build security baselines for IoT 2.0: cert‑based auth, network segmentation, anomaly alerts.

For employees and creatives

  • Learn to supervise agents: write clear specs, set tests, and review outputs.
  • Practice multimodal prompting—text, voice, image—to speed your work.
  • Trial AR glasses or wearables where they save time: field service, training, or live translation.

For policymakers and clinicians

  • Push privacy‑first AI with on‑device processing where possible (policy notes).
  • Require validation on real‑world data before healthcare AI goes live.
  • Invest in accessible assistive tech like BCI and vision/hearing aids.

Conclusion

The next 24 months will feel different because our tools will act more like teammates and our environments will feel alive.
Agents will run processes, devices will infer on‑device, robots will handle real work, and AR, wearables, and BCI will bring computing closer to our senses.

Start small but start now. Automate a full process. Pilot AR for one workflow. Move a model to the edge. Set clear rules for privacy and safety. The organizations that treat these 2026 technology trends as a playbook—not a headline—will move faster, save more, and build trust.

FAQ

What are the most important AI trends 2026 for businesses?

Three to prioritize:

  • End‑to‑end workflow automation with agents.
  • On‑device AI and edge chips for speed and privacy.
  • Robotics in logistics and retail for throughput and safety (industry examples).

How will AR glasses and extended reality change daily life?

You’ll glance for translations, navigation, and captions instead of grabbing your phone. Work training and remote help will feel like a guided overlay, not a PDF. Expect more time in immersive spaces as networks improve — see usage estimates from recent forecasts.

Are humanoid robots 2026 realistic outside labs?

Yes, in controlled sites. Expect pilots in manufacturing and logistics where tasks are repeatable. Costs are dropping, and safety/autonomy are reaching usable levels (examples).

What does quantum computing progress 2026 mean for my team?

Don’t wait for “general quantum.” Explore hybrid pilots for specific optimization or simulation problems with partners like IBM, Google, IonQ, or Rigetti and measure gains against a classical baseline (read more).

How can we protect privacy as on-device AI grows?

Keep sensitive processing on the device when possible, limit cloud storage, and log decisions for audits. Align to GDPR/CCPA; choose models and apps that support offline or private mode by default (guidance).

Will most online content really be AI-generated by 2026?

Forecasts point to up to 90% AI‑generated content. Build review pipelines, watermark outputs, and define brand voice checks to keep quality high and reduce risk (source).

What’s the near-term value of brain-computer interfaces (BCI)?

BCI adds powerful assistive tech first—thought‑controlled cursors, messaging, and mobility for people with paralysis. It sets the stage for wider human‑computer interfaces later.

How do low-code no-code development platforms fit into IT strategy?

Use low/no code for internal apps and automations where speed matters. Wrap it with governance: data policies, role‑based access, testing, and change control. IT stays the platform owner; teams build safely.

What are the first two steps to act on these 2026 technology trends?

Pick one process to automate end to end with agents and a second initiative to move an AI workload on‑device. Run 8–12‑week pilots with clear metrics, then scale what works (pilot checklist).

Artificial Intelligence in Healthcare: 7 Real-World Breakthroughs Saving Time and Lives

Pablo Etcheverry on September 14, 2025

Cover Image

Artificial Intelligence in Healthcare: 7 Real-World Breakthroughs Saving Time and Lives

Estimated reading time: 10 minutes

Key takeaways

  • Medical AI is already in routine care—FDA-cleared devices and clinical decision support tools are powering faster detection and triage.
  • Seven proven use cases—from at-home ECGs to drug discovery—show measurable impact on time-to-treatment and outcomes.
  • Successful adoption needs validation, clinician oversight, governance, and attention to bias, privacy, and workflow integration.
  • Start with problems that matter, insist on evidence, and scale what proves real-world value. See the PMC review for an evidence summary.

Table of contents

  1. Introduction
  2. 1. Detecting arrhythmias outside the hospital
  3. 2. Early sepsis detection
  4. 3. Seizure-detecting smart bracelets
  5. 4. Skin-checking apps
  6. 5. Stroke detection at CT
  7. 6. Breast cancer detection support
  8. 7. Drug discovery acceleration
  9. Cross-cutting benefits
  10. Risks & responsible adoption
  11. Evaluation & implementation checklist
  12. What this means for patients
  13. The road ahead
  14. Conclusion
  15. FAQ

Introduction

Artificial intelligence in healthcare is no longer theoretical. It now powers FDA-cleared medical devices and clinical decision support tools in hospitals and homes.
These tools help clinicians spot disease earlier, monitor patients safely, and make faster treatment decisions—backed by data, not hype.
We’ll walk through seven proven use cases with outcomes, benefits, limits, and what to watch for when adopting them.
(See the PMC review.)

The paradigm shift: Artificial intelligence in healthcare, right now

  • Medical AI is augmenting diagnostics, patient monitoring and triage, and research—not replacing expert judgment.
  • Real-world tools are improving sensitivity and specificity, cutting time-to-treatment, and easing workflow burden.
  • Many are FDA-cleared medical devices and embedded clinical decision support systems you can deploy today. (See the PMC review.)
  • Expect seven evidence-backed examples across the patient journey, from at-home ECGs to deep learning in medical imaging. (Overview: UpSkillist.)

Keep scrolling to see what’s working now—and where it helps most.

Use case 1: Detecting arrhythmias outside the hospital

Problem

  • Atrial fibrillation (aFib) can come and go. Missed episodes raise stroke risk.
  • Traditional Holter monitors are short-term and inconvenient; symptoms often don’t line up with test windows.

AI solution

  • ECG wearables like AliveCor’s Kardia use on-device AI to analyze rhythm strips for arrhythmia detection, enabling at-home, medical-grade atrial fibrillation monitoring in minutes. Results can be shared with clinicians. (See UpSkillist.)
  • These systems are FDA-cleared for rhythm analysis and integrate with care plans as part of clinician-led follow-up. (AliveCor)

What it looks like in practice

A patient feels “fluttering,” records a 30-second ECG on the spot, and the app flags possible aFib. The tracing and summary go to the care team for review, trending, and shared decision-making.

Impact

  • Moves point-of-care to the patient, capturing elusive episodes faster.
  • Reduces time-to-evaluation for anticoagulation decisions and ablation referrals.

Integration notes

  • Ensure clear pathways for data sharing (portal/EHR) and clinician oversight.
  • Educate patients on proper finger placement and recording conditions to reduce false positives/negatives.
  • Track sensitivity/specificity and build thresholds to avoid alert overload. (See the PMC review.)

Use case 2: Early sepsis detection to save critical hours

Problem

  • Sepsis worsens quickly. Every hour of delay in recognition and treatment raises mortality.
  • Manual screening is inconsistent and can miss early signs within large data streams.

AI solution

HCA Healthcare’s SPOT analyzes real-time vitals, labs, and notes to flag likely sepsis earlier than standard practice.
Alerts route to rapid response teams with protocolized steps. (See the PMC review.)

Evidence and outcomes

  • Reported detection up to six hours earlier vs. clinicians alone.
  • Nearly 30% reduction in sepsis mortality after systemwide rollout and workflow changes. (See the PMC review.)

Workflow tips

  • Build a closed loop: alert → acknowledgment → bedside assessment → order set.
  • Reduce alert fatigue by tuning thresholds, suppressing duplicates, and auditing performance regularly.
  • Track operational metrics like time-to-antibiotics, ICU transfers, and LOS. (See HealthTech Magazine.)

Use case 3: Seizure-detecting smart bracelets

Problem

  • Generalized tonic–clonic seizures can cause injury or death if help is delayed, especially when patients are alone or asleep.
  • Caregivers can’t watch 24/7.

AI solution

  • The Empatica Embrace wristband monitors electrodermal activity and movement. Its AI detects likely generalized tonic–clonic seizures and automatically alerts designated caregivers. It is FDA-cleared as a medical device.
  • Clinical testing has shown ~98% detection accuracy for these events in certain settings, with ongoing work on prediction. (See UpSkillist.)

Impact

  • Faster assistance can reduce harm from falls, hypoxia, or status epilepticus.
  • Data logs support clinical visits and medication adjustments.

Considerations

  • Daily wear matters: comfort, battery life, and water exposure.
  • Privacy: consent for caregiver alerts and secure data handling.
  • False alarms vs. missed events balance; set expectations and review logs with clinicians. (See the PMC review.)

Use case 4: Skin-checking apps for early flagging

Problem

  • Skin cancers, especially melanoma, can be subtle. Delays in evaluation worsen outcomes.
  • Access to dermatology is uneven; many people wait too long.

AI solution

Skin-checking apps analyze photos of lesions against large image libraries to estimate risk in seconds, prompting users to seek professional care when needed.
(Summary in the PMC review.)

Role in care

  • Triage, not diagnosis. These apps can nudge timely visits and prioritize higher-risk lesions.
  • Helpful between annual skin checks or for people with many moles.

Caveats

  • Accuracy depends on lighting, focus, and skin tone; training data diversity matters for equity.
  • Regulatory status varies by market; check indications for use.
  • Always confirm with a clinician—biopsy is the gold standard. (See the PMC review.)

Use case 5: Stroke detection at CT with deep learning

Problem

  • In large vessel occlusion (LVO) stroke, minutes matter. The faster the triage, the more brain you save.
  • CT angiography volumes are high; manual reads and paging add delay.

AI solution

Viz LVO applies deep learning in medical imaging to detect suspected LVO on CT and auto-alert the on-call stroke team via secure apps.
Reported performance shows high sensitivity and specificity across multicenter datasets. (See UpSkillist.)

Impact

  • Shorter door-to-needle and door-to-groin times; more patients get timely thrombectomy.
  • Standardizes triage across spoke–hub networks, especially after hours.

Integration pearls

  • Define escalation: who gets pinged (radiology, neurology, ED, IR) and in what order.
  • Embed alerts into the stroke code pathway; track time stamps automatically.
  • Review false positives/negatives and update protocols to maintain trust. (See HealthTech Magazine.)

Use case 6: Breast cancer detection support

Problem

  • High imaging volumes and subtle findings create variability in reads. Missed cancers and recalls stress patients and teams.
  • Pathology review is labor-intensive; small foci can be overlooked.

AI solution

Deep learning in medical imaging acts as a “second reader” for mammography and as decision support for pathology slides, highlighting suspicious regions and prioritizing studies.
(See the PMC review and UpSkillist.)

Evidence

  • Combined AI + clinician assessments often improve accuracy over clinicians alone, with potential reductions in false negatives and smoother workloads.
  • Benefits depend on local prevalence, reader experience, and presentation of AI outputs; continuous validation is essential.

Best practices

  • Use AI as assist, not autopilot. Radiologists make the final call.
  • Monitor sensitivity/specificity, recall rates, and cancer detection rate before and after deployment.
  • Train users on when to trust, when to override, and how to document reasoning for governance.

Use case 7: Drug discovery acceleration

Problem

  • New drugs take too long and cost too much—development often spans a decade and can cost billions before approval.
  • Early stages are slow: finding the right target, designing molecules, and testing candidates.

AI solution

Drug discovery AI speeds target identification, molecule design, and property prediction. Models can score huge chemical libraries in hours, not months, and simulate “what if” experiments before wet-lab work begins.
DeepMind’s AlphaFold predicted around 200 million protein structures, making protein shape data available to researchers worldwide and jump-starting structure-based design.

Impact

  • Faster hit discovery and better candidate selection reduce wasted cycles.
  • Teams can focus lab time on the most promising leads, improving the odds of success and shortening timelines. (See the PMC review.)
  • Expect tighter links between AI models, robotic labs, and real-world evidence to refine predictions further.

Practical notes

  • Validate in stages: in silico → in vitro → in vivo. Treat AI scores as hypotheses to test, not answers.
  • Watch for generalizability across chemotypes and targets. Build diverse training sets and benchmark often.
  • Track key metrics: hit rate, cycle time per iteration, and downstream attrition.

Cross-cutting benefits of medical AI

  • Earlier detection and intervention—tools that flag sepsis, stroke, or arrhythmias can shave hours off time-to-treatment and save lives. (HealthTech Magazine.)
  • Extending care beyond the hospital—ECG wearables, seizure-detecting wearables, and skin-checking apps bring monitoring and triage into daily life. (UpSkillist.)
  • Workflow efficiency—prioritization, triage, and automation reduce cognitive load and speed handoffs. (PMC review.)
  • Consistency and decision support—clinical decision support systems apply rules and models the same way every time.
  • Data to learn from—AI-enabled devices and platforms generate structured time stamps and outcomes that feed quality improvement.

Risks, limits, and responsible adoption

Validation and generalizability

  • Performance can vary by site, population, scanner, or workflow. Validate locally before scaling.
  • Use prospective studies and monitor real-world drift. Refresh or retrain models when performance slips. (PMC review.)

Bias and equity

  • If training data underrepresent certain groups, models may underperform for them. Audit by age, sex, race/ethnicity, and comorbidity.
  • Co-design with diverse communities and use representative datasets to reduce disparate impact. (PMC review.)

Safety and regulation

  • Confirm regulatory status: FDA-cleared medical devices or clinical decision support that meets defined criteria.
  • Follow indications for use and keep post-market surveillance in place with clear reporting lines. (PMC review.)

Human-in-the-loop

  • Keep clinician oversight. AI suggests; clinicians decide. Document accountability, escalation paths, and overrides.
  • Train users on how outputs are generated, limitations, and when to distrust a result. (HealthTech Magazine.)

Explainability and trust

  • Favor interfaces that show evidence: heatmaps on images, contributing vitals/labs for risk scores, and links to guidelines.
  • Explainability helps adoption, education, and quality review. (PMC review.)

Privacy and security

  • Protect PHI end to end: encryption, access controls, audit logs, and secure APIs.
  • For wearables and apps, get clear consent for data sharing and caregiver alerts. (PMC review.)

Integration realities

  • Poorly tuned alerts cause fatigue. Tune thresholds, suppress duplicates, and review weekly at launch, then monthly. (HealthTech Magazine.)
  • Budget for change management, training, and ongoing monitoring—not just the license.

How to evaluate and implement AI in healthcare (practical checklist)

Clinical evidence

  • Look for peer-reviewed studies with clear outcomes, sensitivity and specificity, and prospective or multicenter designs. (PMC review.)
  • Prefer evidence that includes your patient mix and care setting.

Regulatory and legal

  • Verify FDA or CE status and indications for use. Request the latest instructions for use and known limitations.
  • Map liability: who confirms, who acts, and how overrides are logged.

Workflow fit

  • Define the closed loop: alert routing, acknowledgment, bedside assessment, and standard order sets.
  • Plan EHR integration, device data flows, and escalation roles across teams. (HealthTech Magazine.)

Operations and ROI

  • Track before/after metrics: time-to-treatment, LOS, transfers, readmissions, mortality, and cost per case.
  • Factor soft wins: reduced burnout, faster handoffs, fewer weekend delays.

Governance and quality

  • Set up a clinical-technical governance group for model approval, drift monitoring, and incident review.
  • Require vendor SLAs on uptime, cybersecurity, update cadence, and support.
  • Establish feedback loops to refine thresholds and improve sensitivity/specificity over time. (PMC review.)

Training and change management

  • Run tabletop drills for sepsis and stroke alerts. Use short video tips for wearables and imaging UIs.
  • Name super-users in each unit to champion adoption.

What this means for patients and caregivers

  • Timely alerts. Wearables and apps can flag heart rhythm changes, seizures, or skin lesions sooner so you can act fast. (PMC review.)
  • Easier monitoring. At-home tools cut travel and help your team track trends between visits.
  • Clear next steps. Treat app results as prompts, not diagnoses. Share data with your clinician and ask what action plan to follow.
  • Red flags to avoid. Be cautious with tools that lack medical oversight, hide who reviews your data, or make big claims without evidence. (PMC review.)

How to get the most value

  • Learn correct use (e.g., ECG finger placement, photo lighting).
  • Set consent preferences for caregiver alerts and data sharing.
  • Keep a simple log of symptoms and device alerts to support clinical visits.

The road ahead

  • Prediction gets closer—research aims to forecast seizures, heart failure decompensation, and sepsis hours before onset. (UpSkillist.)
  • Multimodal models—combining vitals, labs, notes, imaging, and wearables will improve accuracy and reduce false alarms. (PMC review.)
  • Better explainability—expect clearer reasons for each flag and tighter links to guidelines and order sets.
  • Standard of care—more AI will be embedded in routine pathways as evidence grows and regulation matures. (PMC review.)

Conclusion

Across homes, clinics, and hospitals, medical AI is helping teams act faster and with more confidence.
From arrhythmia detection to stroke triage and drug discovery AI, the gains are practical: earlier flags, smoother workflows, and better use of expert time.
The right guardrails—validation, oversight, and governance—keep patients safe and equity front and center.
Artificial intelligence in healthcare works best as a partner to clinicians. Start with the problems that matter most, insist on evidence, and scale what proves real-world value.

FAQ

Q: What is “good” accuracy for clinical AI?

A: It depends on use case and risk. For time-critical triage, prioritize sensitivity; for screening, balance sensitivity and specificity and track downstream impact. (PMC review.)

Q: Are these tools replacing clinicians?

A: No. They are clinical decision support. Clinicians confirm findings, make decisions, and stay accountable. (PMC review.)

Q: How do we prevent alert fatigue?

A: Start with narrow indications, tune thresholds, suppress duplicates, and audit alerts weekly during rollout. (HealthTech Magazine.)

Q: What should we ask vendors before buying?

A: Evidence quality, regulatory status, EHR integration, sensitivity/specificity in settings like yours, cybersecurity practices, and support SLAs. (PMC review.)

Q: Can patients rely on skin-checking apps or ECG wearables for diagnosis?

A: No. Use them for triage and monitoring. Share results with your clinician for diagnosis and treatment. (PMC review.)

Q: How is AlphaFold used in real care today?

A: AlphaFold informs research and discovery, not bedside care. It accelerates understanding of protein structures to guide new therapies. (DeepMind.)

Q: What about data privacy with wearables?

A: Choose tools with clear consent, encryption, and limited data sharing. Ask who can see alerts and how data are stored. (PMC review.)

Q: How do we measure success after deployment?

A: Track clinical outcomes (e.g., time-to-antibiotics, door-to-groin), safety (false alerts), user adoption, and financial impact. Review regularly and adjust. (HealthTech Magazine.)

IoT Trends 2025: Key Innovations in Edge AI, 5G, Digital Twins, and IoT Security

Pablo Etcheverry on September 14, 2025

Cover Image

IoT trends 2025: Edge AI, 5G and Satellite IoT, Digital Twins, and Security You Can’t Ignore

Estimated reading time: 10 minutes

Key takeaways

  • Edge AI moves intelligence to devices for lower latency, privacy, and cost savings.
  • Next‑gen connectivity (5G, network slicing, satellite, multi‑carrier eSIM) delivers resilience and predictable performance.
  • Smarter, greener devices—low power chips, RISC‑V, and optimized modules—reduce TCO and enable new use cases.
  • Digital twins let you test and optimize before you act, shortening improvement cycles.
  • Security by design and rising regulation make end‑to‑end security and SBOMs mandatory for scale.

Table of contents

Introduction: Why IoT trends 2025 matter now

IoT is changing fast. Billions of devices are going online. New networks reach places Wi‑Fi never could.
AI is moving from the cloud to the device. And rules for security are getting stricter by the month.
These IoT trends 2025 will shape your roadmap—and your results.
Sources: Jaycon,
KaaIoT.

This guide gives you:

  • Clear language, no hype
  • Real examples in factories, smart cities, and healthcare IoT
  • Short action checklists you can use this quarter

Keep reading to see what to adopt now, what to test soon, and what to avoid.

Trend 1 — Edge AI: Real-time intelligence at the point of action

What it is

Edge AI
joins edge computing with embedded AI models. Devices run on‑device inference close to the data—on a camera, a gateway, or a machine controller.
No round trip to the cloud for every decision. This is AIoT in practice.

Why it matters

  • Lower latency: milliseconds, not seconds
  • More privacy: less sensitive data leaves the site
  • Lower cloud costs: fewer uploads and less compute
  • Higher uptime: devices keep working if the link drops — source

Where it impacts

  • Hospitals: monitor vitals and detect risk at the bedside
  • Factories: quality inspection on the line; predictive maintenance
  • Smart cities: traffic signal timing that adapts in real time to reduce congestion

Example: A smart camera flags defects as parts roll by. It triggers a reject gate in under 50 ms.
The cloud still gets summaries for audit and model updates — but not every frame.

Enablers

  • AI‑optimized chips for low power consumption and fast inference
  • Compact models via pruning and quantization
  • Toolchains that deploy models to microcontrollers and edge modules

Action checklist

  • Identify latency‑sensitive use cases for edge AI (safety, quality, downtime)
  • Prioritize predictive maintenance pilots in industrial settings
  • Design data flows that minimize cloud round‑trips while preserving auditability (hashes, summaries, and SBOM ties)
  • Plan a model ops loop: collect edge feedback, retrain in the cloud, push signed updates OTA

Up next: to make edge AI sing, you need stronger pipes. Let’s talk 5G IoT, network slicing, and satellite IoT.

Trend 2 — Next‑gen connectivity: 5G IoT, network slicing, and satellite IoT

5G for critical workloads

5G brings deterministic latency and QoS. With network slicing,
you carve out a dedicated “lane” for your traffic. Think of one slice for emergency services, another for autonomous carts,
and a third for plant sensors. Each gets its own rules and guarantees.

Why it matters:

  • Predictable performance for robots, AGVs, and remote operations
  • Private or public 5G options for on‑prem control and security
  • Better density: more devices per cell — source

Satellite IoT to fill coverage gaps

Not every asset lives under a tower. Low‑Earth‑orbit satellites—
Starlink, Amazon Kuiper, OneWeb—cover oceans, deserts, and rural roads. Satellite IoT keeps sensors talking when trucks cross borders,
ships leave port, or pipelines run through remote fields.

Top use cases:

  • Logistics: track fleets end‑to‑end
  • Maritime: vessel telemetry and safety
  • Mining and energy: monitor remote sites
  • Rural infrastructure: water, power, and environmental sensors

Multi‑carrier connectivity

Devices should not get “stuck” on a weak network. With multi‑carrier connectivity and
GSMA eSIM (SGP.32), devices can swap profiles and roam across carriers automatically.
The result: higher uptime and simpler global deployments.

Practical tips

  • Choose modules that support eUICC/eSIM and fallback options
  • Test handover between carriers, 5G, LTE‑M, and NB‑IoT
  • Monitor signal quality and switch by policy, not by guesswork

Action checklist

  • Map coverage needs; combine 5G with satellite IoT for resilience
  • Classify traffic (safety, control, telemetry) and define slices for mission‑critical apps
  • Validate SLAs across multi‑carrier providers; test failover scenarios
  • Document latency budgets end‑to‑end: sensor → gateway → network → app

Trend 3 — Smarter, cheaper, and greener devices

AI‑optimized chips

New edge modules run AI fast and cool. They enable real‑time analytics on‑device, cut latency, and reduce cloud spend.
You get better accuracy where it counts—at the point of action. Source

Low power consumption

Low‑power designs stretch battery life and slash truck rolls. Combine efficient radios (LTE‑M/NB‑IoT), sleep modes,
and compact models to extend service intervals from months to years. Your TCO drops as batteries last longer and data plans shrink.
Source

Open‑source innovation with RISC‑V

RISC‑V enables custom, affordable chips. Teams can tune cores for cost, performance, and power,
then pair with AI accelerators. This speeds experimentation and reduces vendor lock‑in.

Business impact

  • Lower total cost of ownership via power savings and fewer cloud calls
  • Wider feasibility: deploy in places without power or with tiny data budgets
  • Faster iterations: modular designs swap in AI‑capable edge computing when needed

Example: A battery‑powered vibration sensor runs on‑device inference. It streams only anomalies, not raw waveforms.
Battery life jumps from 6 months to 2+ years. Cloud bills fall. Maintenance gets proactive.

Action checklist

  • Update hardware roadmap to include low‑power SKUs and AI‑capable edge modules
  • Evaluate RISC‑V for cost‑sensitive or customizable designs
  • Recalculate TCO using new power and cloud egress assumptions
  • Pilot 5G RedCap or LTE‑M modules where bandwidth and power need a middle path — source

Trend 4 — Digital twins: From visibility to optimization

Definition

A digital twin is a live virtual model of an asset, system, or process. It syncs with IoT data to mirror the real world.
You can watch state, test “what if,” and predict outcomes—before you move a bolt.
Source

Use cases

  • Factories: prevent equipment failures; simulate throughput and staffing
  • Smart cities: optimize traffic flow, lighting, and energy
  • Healthcare: track medical equipment, utilization, and maintenance windows — source

Value

  • Scenario testing: try 10 plans in software, execute only the best one in reality
  • Faster iteration: shorten improvement cycles from months to weeks
  • Measurable savings: less downtime, better utilization, lower energy

Analogy: Think of a flight simulator for your operations. Train, test, and tweak—without risking the plane.

Action checklist

  • Start with high‑impact assets; define KPIs (downtime, throughput, utilization)
  • Integrate OT/IT data sources; ensure data quality and model fidelity
  • Pilot simulations before physical changes to reduce risk
  • Close the loop: use results to adjust edge AI rules and connectivity policies

Keep going: up next we’ll tackle security and regulation you can’t ignore—and a 2025 roadmap to pilot, measure, and scale.

Trend 5 — Security and regulation: End-to-end by design

The threat picture

IoT attacks hit fast and spread wide. Weak passwords, open ports, and old firmware invite trouble.
The risks include data theft, DDoS, ransomware, and safety impacts on real equipment.
Supply chain gaps make it worse if parts are not verified or patched. Source

Regulation is rising

Rules now push “security by design” from day one:

  • EU Cyber Resilience Act: build in protection, manage vulnerabilities, and support updates across the lifecycle.
    Expect proof like SBOMs and update policies — source
  • U.S. Cyber Trust Mark: a consumer label for devices that use strong security practices (encryption, updates, default settings) —
    source
  • UK IoT security legislation: bans default passwords, requires clear update policies, and mandates a way to report bugs —
    source

Security fundamentals to adopt now

  • End‑to‑end security: encrypt data in transit and at rest
  • Strong authentication: unique creds, mutual TLS, hardware root of trust
  • Secure boot: verify firmware on startup; block unsigned code
  • Secure updates: signed firmware, OTA updates, rollback protection
  • SBOMs: track software components; scan for CVEs; fix fast
  • AI‑powered threat detection: spot anomalies at the edge and in the cloud
  • Least privilege: device identity, scoped API keys, and role‑based access
  • Network hygiene: zero trust, micro‑segmentation, and network slicing for critical traffic
  • Key management: rotate keys and certificates; store secrets in secure elements
  • Supply chain security: verify vendors, test components, seal devices with tamper evidence

Think lifecycle, not point fixes

Plan for secure provisioning, daily operations, patching, and decommissioning.
Define update SLAs. Monitor with alerts and logs. Wipe and retire devices safely.
Keep compliance docs complete and current.

Action checklist

  • Map which rules apply (EU, US, UK) and align design to “security by design.”
  • Require secure boot, signed firmware, and OTA updates in all new RFPs.
  • Produce SBOMs for every build; automate vulnerability management.
  • Enable AI‑powered threat detection and anomaly alerts.
  • Run pen tests before launch; re‑test after each major update.
  • Document a secure decommission flow (credential revoke, data wipe).

Implementation roadmap: Turning 2025 trends into wins

Step 1 — Prioritize by value

  • Pick 2–3 use cases with clear ROI: safety, downtime, quality, or energy savings.
  • For each, define latency needs and data flows. If milliseconds matter, pair edge AI with 5G IoT or private 5G. Source

Step 2 — Architecture blueprint

  • Edge‑first: do on‑device inference; send summaries to the cloud.
  • Connectivity mix: 5G network slicing for critical traffic; multi‑carrier connectivity with GSMA eSIM (SGP.32); satellite IoT for remote sites. 3GPP · GSMA · PondIoT
  • Digital twins: mirror assets; test “what if” before any change.
  • Security by design: encryption, authentication, secure boot, signed OTA, and continuous monitoring.

Step 3 — Budget and TCO

  • Account for low power consumption and longer battery life (fewer truck rolls).
  • Include lower cloud egress from on‑device inference.
  • Consider 5G RedCap or LTE‑M for a balanced cost/performance path. Source

Step 4 — KPIs to track

  • Operations: downtime, first‑pass yield, throughput
  • Performance: end‑to‑end latency, SLA adherence, jitter
  • Cost: data usage, battery life, maintenance trips
  • Risk: security incidents, patch SLA, vulnerability backlog
  • Twin value: simulation cycles run, savings per change implemented

Step 5 — Team enablement

  • Train on edge ML ops, model compression, and OTA model updates
  • Upskill network teams on 5G slicing, QoS, and multi‑carrier policies
  • Build digital twin skills: modeling, calibration, and scenario design
  • Level up security practice: SBOMs, secure boot, firmware signing, and incident response — source

Industry mini‑scenarios

Manufacturing

  • Edge AI inspects parts in real time; rejects defects on the line.
  • Predictive maintenance cuts unplanned stops; alerts before a bearing fails.
  • Digital twins test staffing and buffer changes to lift throughput.
  • Private 5G with network slicing protects robot control traffic; LTE‑M handles noncritical telemetry. Source

Smart cities

  • 5G IoT links cameras and signals; slices reserve bandwidth for emergency vehicles.
  • Digital twin of roads optimizes signal timing and reduces congestion.
  • Satellite IoT covers rural water pumps; multi‑carrier connectivity keeps meters online. Source

Healthcare IoT

  • Edge analytics watch vitals at the bedside; alerts fire in milliseconds.
  • Asset tracking + digital twins improve equipment use and maintenance windows.
  • Security by design protects PHI: encryption, authentication, secure updates, and SBOMs. Source

Conclusion: The 2025 IoT playbook

Edge AI, next‑gen connectivity, smarter devices, digital twins, and security by design form a single system.
Start small, where latency and uptime matter most. Use an edge‑first design, with 5G IoT or multi‑carrier connectivity and satellite IoT when needed.
Keep end‑to‑end security in scope from the first sprint. Measure, learn, and scale.

Pick one pilot per site. Define clear KPIs. Prove the gain, then expand. Align each step with your compliance path and budget.

These IoT trends 2025 are not buzzwords—they are your roadmap to safer, faster, and leaner operations.
Now is the time to build, test, and win.

FAQs

What is the difference between edge AI and edge computing?

  • Edge computing moves processing close to the device to cut latency.
  • Edge AI adds on‑device inference, so devices can decide, not just relay data.
  • In short: all edge AI uses edge computing, but not all edge computing runs AI.

When should I choose 5G vs Wi‑Fi 6 for IoT?

  • Choose 5G for mobility, wide outdoor areas, tight latency, or network slicing/QoS.
  • Choose Wi‑Fi 6 for indoor sites with fixed assets and high local throughput.
  • Many sites use both: 5G for critical or mobile gear; Wi‑Fi for local dashboards. Source

How does satellite IoT impact latency and cost?

  • Satellite IoT offers coverage where no towers exist.
  • Latency and cost per MB are higher than 5G/LTE, so send small, smart payloads.
  • Use satellite for remote telemetry, not for heavy video feeds. Source

What are must‑have IoT security features in 2025?

  • Unique credentials, mutual TLS, secure boot.
  • Signed firmware, OTA updates, and rollback protection.
  • End‑to‑end encryption, SBOMs, vulnerability management, and AI‑powered threat detection.
  • Clear update policy and secure decommission steps. Source · Source

How do digital twins reduce operational costs?

  • They let you test changes in software before touching the line.
  • You find better settings faster and avoid bad downtime.
  • Energy, maintenance, and labor plans get smarter with each simulation. Source

Edge AI for IoT: Revolutionizing Intelligent Devices with LLMs, Synthetic Data, and Advanced Hardware

Pablo Etcheverry on September 14, 2025

Cover Image

Edge AI for IoT: How LLMs, Synthetic Data, and New Hardware Make Intelligent Devices Practical

Estimated reading time: 8 minutes

Key takeaways

  • Edge AI compresses cost, power, and latency by moving inference next to sensors rather than streaming raw data to the cloud — see the comprehensive guide to Edge AI in IoT.
  • Synthetic data and LLMs/foundation models accelerate labeling and cover rare cases, reducing time to robust models (CEVA 2025 Edge AI Technology Report).
  • Cascading inference (tiny gate → local detector → cloud explainers) cuts radio use and battery drain while preserving actionable insight (Particle guide).
  • Pick hardware to fit the job: MCUs+NPUs for months on battery, MPUs for multi‑camera Linux apps, GPUs/accelerators for robotics-grade workloads (CEVA report).

Why Edge AI for IoT now?

Edge AI turns messy, continuous signals into actionable events right on the device.
The payoff is clear: you get intelligence without exploding bandwidth, latency, or battery budgets —
read the comprehensive guide to Edge AI in IoT.

Edge AI cuts waste where it hurts most:

  • Bandwidth savings: Process locally and send only results, not raw video or audio streams. A camera can run detection on-device and transmit a tiny alert instead of streaming 30 FPS video (Particle guide).
  • Power efficiency: Moving inference onto microcontrollers with NPUs slashes radio and compute energy, enabling long battery life and making low‑power backhaul viable (CEVA 2025 Edge AI Technology Report).
  • Latency & privacy: On‑device ML gives instant results and keeps raw data local — useful for regulated sites or weak links (Edge AI solutions for smart devices) — also discussed in the Particle guide.

Before: stream 30 FPS to the cloud — pay bandwidth and burn battery.
After: run detection locally and send a 1–2 KB alert over LoRa only when needed (Particle guide).

TL;DR: Move compute closer to sensors to collapse cost, power, and latency at once.

From heuristics to learning systems at the edge

Rule‑based logic looks neat in slides, but real sites are messy: lights flicker, shadows move, motors vibrate.
Heuristics like “if pixel count > X, raise alarm” break fast. Models adapt.

Why learning systems win:

  • They capture patterns beyond thresholds and scale across variability and edge cases (Mirantis guide).
  • They improve as you collect examples and can be updated over time (Particle guide).

Mental model:

  • Heuristics = brittle rulers.
  • Models = flexible lenses.

Practical tip: Start with a tiny anomaly detection model on-device to filter the stream and flag interesting moments — cut bandwidth while you learn what matters.

Data strategy powered by LLMs and foundation models

Great edge models start with great data. LLMs and vision-capable foundation models make that data cheaper and faster:

  • Synthetic data: When real data is scarce or risky, generate it. This works well for audio, time‑series, and simple vision (CEVA report).
    • Keyword spotting: synthesize many voices and backgrounds.
    • Safety events: simulate “glass breaking” sounds.
    • Vibration: create fault signatures at varied speeds.
  • Data quality over quantity: Use vision-capable LLMs to create simple, binary labels (e.g., “Is there a hard hat in this image? yes/no”). Clean labels beat large, messy datasets (CEVA report).
  • Label automation: Let models pre-label and have humans spot‑check low‑confidence items to catch drift and bias early (CEVA report).

Workflow to copy:

  1. Capture a seed dataset from your device.
  2. Generate synthetic variants to cover rare cases.
  3. Run auto‑labeling with LLMs/foundation models for simple questions.
  4. Have humans validate a random slice (10–20%) and low‑confidence items.
  5. Retrain and push a small on‑device model update.

The result: a dataset that stays matched to the real world your device sees.

Hardware landscape for Edge AI (3 + 1 layers)

Choosing hardware is about fit: match workload, latency, power, and cost.

MCUs and MCUs with NPUs

Ultra‑low‑power workhorses. Microcontrollers with NPUs deliver large speedups at tiny power.
Arm Ethos is licensable IP used in embedded SoCs and vendor accelerators like STM32N6 and others (CEVA report).

  • Public demos show YOLOv8 on MCU‑class power achieving usable FPS for small scenes (CEVA report).
  • Best for: keyword spotting (KWS), anomaly detection, simple vision where LoRa or BLE is the backhaul.

MPUs (Linux‑class)

Use when you need more memory, Linux tooling, or multi‑sensor fusion. Platforms from NXP and Renesas target mid‑range vision and audio workloads (CEVA report).

High‑end edge (GPUs and dedicated AI accelerators)

For robotics, AMRs, and heavy inspection lines where mains power is available and ultra‑low latency is required.

Choosing the right tier — rules of thumb

  • If you need months on a battery, start with microcontrollers with NPUs.
  • If you need multi‑camera and the Linux ecosystem, pick MPUs.
  • If you need heavy perception and parallel models, go high‑end.

Prototype on the smallest tier that meets accuracy — quantize and compress first; move up only if needed (Particle guide, CEVA report).

System pattern — cascading inference for bandwidth and cost savings

Cascading inference runs cheap models first and escalates only when needed — a three‑stage flow that saves radio and battery without losing insight.

  1. Stage A: tiny anomaly detector next to the sensor (frame differencing, spectral energy, vibration envelopes).
  2. Stage B: specialized classifier/detector on flagged windows (quantized YOLOv8 on MCU or compact audio/time‑series models).
  3. Stage C: if confidence is low or rich context is required, send a short burst to the cloud for a vision‑capable LLM or foundation model to explain.

Escalation notes:

  • If your device has an NPU (STM32N6 or Arm Ethos‑enabled SoC), run Stage B locally to retain bandwidth savings (CEVA report).
  • If not, forward selected frames to a gateway or the cloud only on anomalies; a few frames per day is cheap compared to constant streaming (Particle guide).

Demo: Most of the time, nothing is sent. When movement occurs, Stage B runs a small detector. If confidence is low, upload 2–3 frames and let a cloud LLM return a narrative like “beer bottles detected; count ≈ 6; one bottle lying on its side” — store only the summary and alert operators (Particle guide).

Why it works: cheap models run often; expensive models run rarely. Event‑driven messages replace continuous streams, shrinking radio time and battery drain (Particle guide).

Building with Edge Impulse (practical workflow)

Edge Impulse is an end‑to‑end lane from raw signals to on‑device ML across audio, time‑series, and simple vision.

What you can do:

  • Ingest sensor data from dev kits or your own boards.
  • Design features and models in the browser or CLI.
  • Optimize (quantize, prune) and export portable C/C++ inference targeting MCUs, MPUs, and accelerators.

Typical pipeline:

  1. Data capture: log hours/days including edge cases (night shifts, rain, different operators).
  2. Augment: add synthetic data for rare cases (accents, simulated faults) (CEVA report).
  3. Auto‑label: use LLMs/vision models for binary questions (e.g., hard hat present?) (CEVA report).
  4. Feature engineering: mel‑spectrograms for audio, spectral peaks for vibration, simple frame preprocessing.
  5. Model selection: 1D CNNs for vibration, CRNNs for audio, compact detectors for images.
  6. Optimize: INT8 quantization, pruning, operator fusion to run on MCU‑class targets.
  7. Deploy: export libraries or firmware and flash to STM32N6, NXP Linux boards, or higher‑end targets.

Developer accessibility: sign up free — many features and generated models are usable commercially, shortening prototype-to-pilot time.

Implementation checklist and best practices

Define the use case and constraints

  • Sensors: camera, mic, accelerometer, temperature?
  • Latency: instant action vs daily summary?
  • On‑device vs cloud split: what must stay local for privacy?
  • Connectivity: LoRa, LTE‑M, Wi‑Fi — budget the payloads.
  • Safety/regulatory: what can you store or transmit? (Edge AI solutions for smart devices)

Data plan

  • Real‑world sampling across sites, shifts, seasons.
  • Synthetic data for rare faults and edge conditions (CEVA report).
  • LLM‑assisted labeling with human validation for low‑confidence items (CEVA report).
  • Governance: versioning, consent, retention.

Model plan

  • Start simple: small anomaly detection gate first.
  • Choose architectures by modality and optimize early (quantization, pruning) (CEVA report).

Hardware selection

  • Months on a battery → microcontrollers with NPUs (Arm Ethos, STM32N6) (CEVA report).
  • Linux, storage, multi‑camera → MPUs (NXP, Renesas).
  • Heavy sensor fusion → GPU/accelerator gateway.

Edge‑cloud orchestration

  • Use cascading inference to minimize traffic.
  • Send LoRa alerts with small metadata; upload frames only on escalation (Particle guide).
  • Plan OTA model and firmware updates with gradual rollouts.

Validation and operations

  • Log confidences, drift scores, and power draw.
  • A/B test model versions on small cohorts.
  • Schedule re‑labeling and re‑training as environments change (Mirantis guide).

ROI metrics

  • Bytes sent per day vs baseline.
  • Device runtime per charge vs baseline.
  • Time‑to‑detect and time‑to‑act.
  • Accuracy vs cost: precision/recall per dollar of BOM + backhaul (Particle guide, CEVA report).

Risks, constraints, and how to mitigate them

  • Model generalization
    Risk: a single model that tries to do too much will underperform.
    Mitigation: narrow scope and ship multiple small models (Mirantis guide).
  • Data drift and environment change
    Risk: lights, layouts, and machinery change over time.
    Mitigation: monitor anomaly and false alarm rates; schedule re‑labeling and retraining; keep a rolling buffer for audits (Mirantis guide).
  • Privacy and compliance
    Risk: raw images or audio may capture sensitive info.
    Mitigation: keep raw data local; transmit summaries or alerts unless escalated and approved (Particle guide, BombaySoftwares).
  • Compute and memory limits
    Risk: models won’t fit or run fast enough.
    Mitigation: leverage NPUs, efficient operators, quantization, and cascading inference; choose hardware with Arm Ethos or STM32N6‑class accelerators when needed (CEVA report).
  • Bias and labeling errors
    Risk: bad labels or skewed data degrade accuracy.
    Mitigation: use labeling automation with human review and test on new sites before broad rollouts (CEVA report).

Conclusion

Smart edge devices are practical today. Mature sensing and connectivity pair with on‑device ML, LLM‑assisted data workflows, and capable low‑power silicon to deliver reliable results at low cost.
Synthetic data and foundation models let you build datasets quickly. Microcontrollers with NPUs and Arm Ethos‑based SoCs let you deploy real models at ultra‑low power. Cascading inference yields huge bandwidth savings without losing insight (Particle guide, CEVA report).

Your next step: pick one narrow use case, build a tiny anomaly detector, and wire up event‑driven messaging over LoRa. Use Edge Impulse to move from data capture to deployment in days, not months. This is the moment to ship real value with Edge AI for IoT.

Optional resource: grab a fundamentals book on Edge AI for sensor data and pattern design to guide your team’s playbook.

FAQ

What is cascading inference?

It’s a layered approach: a tiny gate model runs all the time and only triggers heavier analysis on interesting events.
This cuts radio use and power while keeping accuracy where it matters (Particle guide).

Do I need an NPU to run vision on a battery device?

Not always, but NPUs help a lot. Microcontrollers with NPUs (e.g., STM32N6 or Arm Ethos‑enabled SoCs) can run compact detectors at MCU‑class power, enabling long battery life (CEVA report).

Can LoRa carry video?

No. LoRa is for small payloads. Use it to send alerts, counts, and metadata. Escalate to higher‑bandwidth paths when needed (Particle guide).

How do LLMs help if models run on the device?

LLMs and vision foundation models supercharge the data pipeline: synthetic data, auto‑labeling at scale, and rich explanations in the cloud during escalations (CEVA report).

Is synthetic data reliable?

Yes, when validated. Use synthetic data for rare cases and spot‑check with humans. Blend with real data and re‑train as you collect more field samples (CEVA report).

How often should I re‑train?

Start with monthly re‑training during pilots, then adjust based on drift signals and false alarm rates. Re‑train sooner after site changes or new SKUs (Mirantis guide).

What about privacy?

Keep raw data on the device whenever possible. Transmit summaries, not streams, and use strict access controls for escalated uploads (BombaySoftwares).

Can YOLOv8 run on a microcontroller?

Small variants can, when quantized and pruned — especially on STM32N6‑class NPUs. Public demos show usable FPS for simple scenes (CEVA report).

How do I pick between MCU, MPU, and GPU?

Map workload, latency, and power: months on battery → MCU+NPU; multi‑camera Linux apps → MPU; heavy parallel workloads → GPU/accelerator (CEVA report).

What ROI should I expect?

Track reduced bytes sent, longer device runtime, faster detection, and fewer false alarms.
Teams often see step‑change gains when moving from cloud‑only to IoT edge computing with cascading inference (Particle guide).

Where should I start today?

Pick one narrow use case. Build a Stage‑A anomaly detection model in Edge Impulse. Deploy to a dev board with an NPU, send LoRa alerts, and iterate — the fastest path to proving value with Edge AI for IoT.

Hermes 4 LLM: Advancing Open-Weight Reasoning with Hybrid Transparency and Google RLM Insights

Pablo Etcheverry on August 30, 2025

Cover Image

Hermes 4 LLM: How Nous Research Pushed Open‑Weight Reasoning to the Edge—and Why Google’s RLM Matters Too

Estimated reading time: 12 minutes

Key takeaways

  • Hybrid reasoning, visible on demand: Hermes 4 offers chain‑of‑thought transparency you can toggle, enabled by a synthetic data pipeline and verification suite (DataForge / Atropos video).
  • Training choices matter: long traces, multi‑path solutions, and a dedicated “when to stop” fine‑tune reduce runaways while preserving most accuracy (training details).
  • Open‑weight, production‑ready: Hermes 4 models (14B, 70B, 405B) are available for inspection and custom finetuning (openrouter, Hugging Face).
  • Complementary approach — Google RLM: RLM reframes regression as text‑to‑text prediction for system telemetry with fast adaptation and strong accuracy (Google Research blog).
  • Practical pairing: use Hermes 4 for explainable plans and proofs; use RLM for numeric forecasts and ranking from structured telemetry.

Table of contents

  1. Context: Why Hermes 4 matters now
  2. What is the Hermes 4 LLM?
  3. How hybrid reasoning & chain‑of‑thought are taught
  4. DataForge synthetic training data
  5. Atropos verifiers & quality control
  6. Training discipline: teaching “when to stop”
  7. Benchmarks and performance
  8. Training engineering & hardware
  9. Practical implications & fits
  10. Limitations & considerations
  11. Google RLM: text‑to‑text regression
  12. How Hermes 4 and RLM complement each other
  13. Getting started & resources
  14. Conclusion
  15. FAQ

Context: Why Hermes 4 matters now

Open‑weight models are closing the gap on hard reasoning tasks. Teams can inspect, fine‑tune, and deploy high‑skill models without a closed API in the loop. Nous Research Hermes 4 arrives in this moment with a clear goal: hybrid reasoning you can see and steer, backed by open benchmarks. See the model entry on OpenRouter.

What is the Hermes 4 LLM?

Hermes 4 ships in three sizes: 14B, 70B, and 405B. The 405B “monster” is built on the Llama 3.1 405B backbone and then pushed through intense post‑training to improve reasoning quality. Read the 70B model card on Hugging Face and the 405B details on OpenRouter.

Core philosophy:

  • Squeeze performance from post‑training, not just pretraining.
  • Make reasoning visible and controllable.
  • Enforce formats, schemas, and stop signals without killing accuracy.

Hybrid behavior: simple queries return short answers; hard problems can surface chain‑of‑thought inside tags such as <think> ... </think>. Toggle this mode with prompts. See a model listing on LM Studio.

How hybrid reasoning & chain‑of‑thought transparency are taught

Hermes 4 treats chain‑of‑thought as an internal trace. The model learns when to surface that trace using tags, making thinking steps first‑class rather than an accident of sampling. (Source material and demonstrations are available in the DataForge / Atropos video.)

Benefits:

  • Auditability — check each step and find slips.
  • Debugging — catch math or logic errors early.
  • Pedagogy — students see the why, not just the what.

Guardrails: ask for “final answer only” and Hermes stays terse; wrap with reasoning tags and it lays out the path. Schema checks and stop signals ensure the output shape even when traces are long.

DataForge synthetic training data: the engine behind Hermes 4

Instead of scraping messy web text, Hermes 4 uses DataForge — a synthetic pipeline that builds diverse, high‑quality reasoning examples on purpose. Watch the pipeline description on YouTube.

Graph‑based transformations: samples are treated like graphs with inputs, requirements, outputs, and transformations (PDDL‑like planning). This lets the team compose hard tasks while preserving structure and checks.

Example pipeline:

  1. Start with a Wikipedia article (e.g., photosynthesis).
  2. Transform it (e.g., rewrite as a short rap with the key facts).
  3. Decompose into Q&A, step‑by‑step explanations, and small quizzes.
  4. Generate multiple valid reasoning traces to teach strategy diversity.

Scale and length: roughly 5 million samples (~19B tokens) with long traces up to 16k tokens, teaching the model to sustain reasoning.

Atropos reinforcement learning verifiers: quality control

Atropos is the verification gauntlet that stress‑tests samples and model outputs. It runs a battery of checks before data or outputs pass into training or CI. See the verification pipeline described in the video.

What runs in the gauntlet:

  • 1,000+ verifiers (math, code, science, safety).
  • Format checks across 150+ templates (JSON, YAML, tables).
  • Instruction following tests, rubric scores, and schema validation (Pydantic).
  • Tool‑use simulations to test agentic flows.

Multiple valid solution paths are preserved: if different traces solve the problem, both can pass. This teaches strategy diversity and reduces brittleness.

Tackling rambling: training Hermes 4 to stop at the right time

The challenge: long reasoning traces can run away and hit context limits. Hermes 4 adds a second fine‑tuning stage focused solely on closing and end‑of‑sequence signals.

Method:

  • Generate ultra‑long traces and cut them (~30k tokens).
  • Insert closing tags and end signals.
  • Fine‑tune only on stopping tokens so “how to reason” and “when to stop” are learned separately.

Measured impact: large reductions in runaway generations (AIME’24 −78%, LiveCodeBench ~−80%), with accuracy held within ~5–12% depending on the benchmark. See the training overview on YouTube.

Benchmarks: open‑weight state‑of‑the‑art reasoning

The 405B Hermes 4 posts competitive open‑weight reasoning numbers across math, science, and code. Headline public results include (reported by the team):

  • MATH500: 96.3%
  • AIME’24: 81.9%
  • AIME’25: 58.1%
  • GPQA Diamond: 70.5%
  • LiveCodeBench: 61.3%

RefusalBench (alignment style): Hermes 4 in reasoning mode reports 57.1% compared with lower numbers for some closed models, suggesting a neutral but auditable engagement policy (source).

Smaller variants (14B, 70B) inherit the same recipe; exact scores vary by size. See the 70B card on Hugging Face and the 405B page on OpenRouter.

Training engineering and hardware choices

Hermes 4 training used heavy compute (192 NVIDIA B200 GPUs) combined with careful engineering. See the overview on the project site: hermes4.nousresearch.com.

Efficiency tactics:

  • Long sequences with minimal padding (token packing).
  • Important‑token learning: focus updates where the signal is strong.
  • Careful learning‑rate schedules.
  • Stacked parallelism: mix data, tensor, and pipeline parallelism for smooth scaling.

The implication: with smart pipelines and verification, open teams can reach near‑frontier results and support private finetunes and reproducible research.

Practical implications: when to reach for Hermes 4

Hermes 4 is ideal when you need clear steps and control over output format. Good fits include:

  • Math problem solving and proof checking
  • Code reasoning, debugging, and test planning
  • Scientific Q&A with citations and step lists
  • Explainable tutoring and walkthroughs
  • Agent workflows that must obey schemas and tool‑call patterns

Why transparency matters: audit trails for regulated domains, faster root‑cause analysis, and clearer teaching materials.

Open‑weight benefits: finetune on private corpora, deploy on your GPUs/VPCs, and inspect safety policies. Prompt tips: use terse mode for throughput and enable chain‑of‑thought for debugging. See model listings on OpenRouter and Hugging Face.

Limitations and considerations

Plan for trade‑offs:

  • Some accuracy dips after strict “when to stop” training — teams trade a small accuracy loss for far fewer runaways (source).
  • Context budgeting matters on 14B/70B variants — long traces consume tokens quickly (70B model card).
  • Alignment stance is neutral and engaging; consider extra governance rules for sensitive domains (source).
  • Inference cost: the 405B model is heavy. Use it where the benefit outweighs latency and cost (OpenRouter).

Pivot to Google RLM: Regression Language Model

Some tasks are about predicting system behavior rather than step‑by‑step reasoning. Google’s Regression Language Model (RLM) reframes this as text‑to‑text regression: you serialize system state as JSON/YAML and the model returns numeric predictions as text. See the Google Research overview at research.google/blog.

Why this matters:

  • System telemetry is structured already — text serialization keeps pipelines simple.
  • Long context allows rich histories or config dumps.
  • Sample multiple predictions to gauge uncertainty.

RLM design: intentionally small (~60M parameters), trained directly on I/O pairs (no broad web pretraining). Key choices include custom number tokenization and very fast fine‑tune cycles — a few hundred examples can get you started. See the Google Research blog for details (source).

Performance: on Borg clusters RLM reports very high rank correlations (up to 0.99) and large MSE reductions versus baselines. Uncertainty is first‑class by sampling multiple outputs (source).

How Hermes 4 and RLM complement each other

Two trends, one toolbox:

  • Transparent problem solving: use Hermes 4 for hybrid reasoning, explanations, and schema‑bound outputs.
  • Structured prediction: use Google RLM for fast, accurate, text‑to‑text regression over system state.

Combined workflow example:

  1. Hermes 4 drafts several candidate plans or scenarios (auditable chain‑of‑thought).
  2. RLM scores each scenario’s predicted performance from telemetry snapshots.
  3. A controller selects the best plan based on scores and uncertainty.

This pairing gives you both a thinking engine and a system oracle.

Getting started & resources

Hermes 4

  • Open weights and model pages: Hugging Face (70B), OpenRouter (405B), project site.
  • Hardware: 14B/70B run on high‑memory single GPUs or multi‑GPU boxes; 405B needs sharding and heavy VRAM (details).
  • Prompts: terse mode = “Final answer only.” Transparent mode = wrap with <think> … </think>. Use JSON templates to enforce schemas (LM Studio).
  • Finetuning: keep Atropos‑style checks in CI to avoid drift (verification video).

RLM

  • Background and papers: Google Research blog, and publications at research.google/pubs.
  • Serialization: choose a stable JSON/YAML schema, normalize units/time zones, include short histories where helpful.
  • Minimal dataset: ~500 labeled I/O pairs to start; split by time and hold out a slice for testing.
  • Training tips: track rank correlation and MSE; sample multiple outputs to estimate uncertainty; retrain as configs change.

Conclusion

Reasoning is not one thing. Sometimes you need a clear proof; sometimes you need a sharp forecast. Hermes 4 provides control, visibility, and open‑weight reasoning you can deploy and inspect. Google’s RLM offers compact, adaptable text‑to‑text regression for system performance.

One phrase to remember: combine transparent reasoning with text‑to‑text regression for system performance.

Hermes 4 LLM is ready for real work; pair it thoughtfully with RLM where numeric forecasts are required. See Hermes 4 on OpenRouter and RLM notes on the Google Research blog.

FAQ

What makes Hermes 4 different from the base Llama 3.1 405B parameter model?

Answer: Hermes 4 keeps the same backbone but applies focused post‑training for hybrid reasoning: chain‑of‑thought transparency, schema control, and disciplined stopping. It is tuned to switch between terse answers and detailed traces on demand. See the OpenRouter entry: openrouter.ai/nousresearch/hermes-4-405b.

How do I enable or disable chain‑of‑thought transparency?

Answer: Use prompts. For short outputs: “Final answer only.” For visible steps: wrap the explanation in <think> … </think>. You can also provide JSON schemas to force structure. See model notes on LM Studio.

Can I fine‑tune Hermes 4 on my domain data?

Answer: Yes. The open weights allow custom finetunes. Keep data in clear formats, include multiple valid solutions, and add verifiers in your training loop to protect structure and correctness. See the 70B card on Hugging Face and verification examples (video).

What hardware do I need to run Hermes 4?

Answer: 14B/70B can run on high‑memory single GPUs or multi‑GPU rigs. The 405B model typically needs multi‑GPU inference with significant VRAM and careful sharding. Plan capacity before production. See OpenRouter: openrouter.ai.

Does the “when to stop” training hurt accuracy?

Answer: The team reports major reductions in runaway generations while keeping accuracy within ~5–12% depending on the benchmark. Many teams accept a small accuracy dip for stable, schema‑complete outputs (source).

What is Google’s RLM in simple terms?

Answer: A small encoder‑decoder that reads structured text (e.g., JSON describing your system) and writes numbers (the predicted metric) as text. It treats regression as language modeling and is fast to fine‑tune. See Google Research.

How does RLM handle uncertainty?

Answer: Sample the model multiple times. The spread of predictions gives an uncertainty estimate you can use in planners, simulators, or risk‑aware schedulers (source).

When should I pick Hermes 4 vs. RLM?

Answer: Choose Hermes 4 for explainable problem solving, tutoring, coding, and drafting policies. Choose RLM when you need a precise metric prediction from telemetry with minimal feature engineering. Many practical systems combine both: Hermes drafts, RLM scores.

Where can I see benchmarks and docs?

Answer: Hermes 4 resources: OpenRouter, Hugging Face, and the project site hermes4.nousresearch.com. RLM background and case studies: Google Research blog and research.google/pubs.

This website uses cookies
Imajine relies on cookies to improve your online experience. Cookies are used to play videos, and to analyze our website traffic.

Allow cookies
Privacy policy