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 dictate notes after each encounter and edit them in the EHR. Your privacy team blocked the voice AI tool your clinical informatics team evaluated because it sends audio to a cloud API. Physician documentation time hasn't changed.
The tool your informatics team wanted was the right clinical idea. The architecture was the wrong answer.
The Four Decisions That Determine Whether This Works
Transcription vs structured note generation. Real-time transcription produces a text record of everything said. Structured note generation takes that transcript and organizes it into a clinical note format. These are two different AI tasks. Transcription is the foundation. Note generation is the layer on top. Building both in one sprint without getting transcription right first is where most clinical voice projects fail — you end up debugging a structuring problem caused by a transcription problem.
Medical vocabulary and proper noun handling. On-device transcription models trained on general audio mis-transcribe drug names, anatomical terms, and procedure names. The model needs medical vocabulary fine-tuning or a post-processing correction layer before it produces transcripts a physician will trust. A transcript with three drug name errors in a 10-minute encounter is not usable for clinical documentation.
Multi-speaker environments. Clinical encounters involve the physician and the patient speaking at different distances from the device. Speaker diarization — knowing who said what — is required before the transcript can be structured into a note. Most general-purpose on-device models don't include diarization. Scoping this before the model is selected avoids building transcription that works in a demo and fails in a real exam room.
Workflow integration. The transcription and note draft have to appear in the physician's existing EHR workflow, not in a separate app they have to switch to. The integration architecture determines whether this saves time or adds a step. A tool that requires a context switch to review a draft costs more time than it saves for most physicians.
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.
Top comments (0)