Short answer: Clinical teams can use AI for documentation and decision support without patient data leaving the device. The model runs on-device, inside your compliance boundary. Wednesday ships these in 4–6 weeks, $20K–$30K, money back.
Your physicians spend 2 hours per day on documentation. Your Privacy Officer has blocked every ambient AI documentation tool on the market because they send audio to cloud transcription APIs. Your burnout survey results are getting worse.
The documentation problem has a solution. The cloud audio problem is the reason it hasn't shipped yet.
The Four Decisions That Determine Whether This Works
Ambient vs prompted documentation. Ambient documentation listens to the entire encounter and structures the note. Prompted documentation asks the physician specific questions at the end of the encounter. Ambient is higher-value but requires a more capable on-device audio model. Prompted is lower-risk to deploy first and delivers faster time-to-value. Teams that try to ship ambient before validating prompted documentation end up debugging two systems at once.
On-device audio processing. Audio never leaves the device if transcription runs locally. The on-device speech-to-text model needs to handle medical terminology, background clinical noise, and multiple speaker voices. Model selection for clinical audio is different from general speech recognition — general models miss drug names, anatomical terms, and procedure codes at rates that make the transcripts unusable.
Note structure and EHR format. A transcription that produces unstructured text isn't useful to a physician who needs a SOAP note in Epic. The post-transcription structuring layer has to map to your EHR's note format and import cleanly without a copy-paste step. The EHR integration is the difference between a tool physicians use and a tool that sits on the shelf.
Physician adoption. Documentation AI only works if physicians use it. The UX for starting a session, reviewing the draft, and correcting errors has to fit into the 3 minutes between patient rooms, not the 20-minute session physicians don't have time for. Adoption design is part of the engineering scope, not a post-launch problem.
Most teams spend 4-6 months discovering these decisions by building the wrong version first. A team that has shipped this before compresses that to 1 week.
On-Device AI vs. Cloud AI: What's the Real Difference?
| Factor | On-Device AI | Cloud AI |
|---|---|---|
| Data transmission | None — data never leaves the device | All inputs sent to external server |
| Compliance | No BAA/DPA required for inference step | Requires BAA (HIPAA) or DPA (GDPR) |
| Latency | Under 100ms on Neural Engine | 300ms–2s (network + server queue) |
| Cost at scale | Fixed — one-time integration | Variable — $0.001–$0.01 per query |
| Offline capability | Full functionality, no connectivity needed | Requires active internet connection |
| Model size | 1B–7B parameters (quantized) | Unlimited (GPT-4, Claude 3, etc.) |
| Data sovereignty | Device-local, no cross-border transfer | Depends on server region and DPA chain |
The right choice depends on your compliance constraints, query volume, and task complexity. Wednesday scopes this in the first week — before any code is written.
Why We Can Say That
We built Off Grid because we hit every one of these problems in production. Off Grid is the fastest-growing on-device AI application in the world, with 50,000+ users running it today.
It's open source, with 1,650+ stars on GitHub and contributors from across the world. It has been cited in peer-reviewed clinical research on offline mobile edge AI.
Every decision named above — model choice, platform, server boundary, compliance posture — we have made before, at scale, for real deployments.
How the Engagement Works
The engagement is four sprints. Each sprint is fixed-price. Each sprint has a named deliverable your team can put on a roadmap.
Discovery (Week 1, $5K): We resolve the four decisions — model, platform, server boundary, compliance posture. Deliverable: a 1-page architecture doc your CTO can take to the board and your Privacy Officer can take to Legal.
Integration (Weeks 2-3, $5K-$10K): We ship the on-device model into your app behind a feature flag. Deliverable: a working build your QA team can test against real workflows.
Optimization (Weeks 4-5, $5K-$10K): We hit the performance and compliance targets from the discovery doc. Deliverable: benchmarks signed off by your team.
Production hardening (Week 6, $5K): Edge cases, OS version coverage, app store and compliance review readiness. Deliverable: shippable build.
4-6 weeks total. $20K-$30K total.
Money back if we don't hit the benchmarks. We have not had to refund.
"Retention improved from 42% to 76% at 3 months. AI recommendations rated 'highly relevant' by 87% of users." — Jackson Reed, Owner, Vita Sync Health
Ready to Map Out Your Clinical AI Deployment?
Worth 30 minutes? We'll walk you through what your clinical workflow, your HIPAA posture, and your on-device target mean in practice.
You'll leave with enough to run a planning meeting next week. No pitch deck.
If we're not the right team, we'll tell you who is.
Book a call with the Wednesday team
Frequently Asked Questions
Q: Can clinical providers use AI without patient data leaving the device?
Yes. On-device inference processes locally and produces a result — a draft note, a suggested code, a flag — without transmitting input to an external server. The compliance boundary is the device itself.
Q: What AI tasks can run on-device for clinical workflows?
Clinical documentation drafting, ICD/CPT code suggestion, discharge summary generation, triage guidance, and referral letter drafting. Tasks requiring real-time EMR lookup still need connectivity.
Q: How long does on-device AI for clinical take?
4–6 weeks: discovery (model, compliance, server boundary), integration, optimization, hardening.
Q: What does on-device AI for clinical cost?
$20K–$30K across four fixed-price sprints, money back if benchmarks aren't met.
Q: Has on-device AI been validated in clinical settings?
Wednesday's Off Grid application — 50,000+ users, 1,650+ GitHub stars — has been cited in peer-reviewed clinical research on offline mobile edge AI, validating the RAG-on-device approach for clinical reference use cases.
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