Short answer: A home health mobile app can run AI on-device and remain HIPAA compliant — patient data never leaves the device, so there is no cloud processor to sign a BAA for. Wednesday ships these integrations in 4–6 weeks, $20K–$30K fixed price, money back.
Your home health aides document visits on phones with no cellular coverage in rural areas. Your compliance team has blocked every cloud AI tool that touches visit notes.
The two facts together define the project. An offline model that processes documentation on the device satisfies both constraints simultaneously. The question is which one to build and how to build it without a 6-month detour.
What decisions determine whether this project ships in 6 weeks or 18 months?
Four decisions determine whether this project delivers value in 6 weeks or becomes a discovery exercise that never ships.
Which model fits the task. Summarization for visit notes is a different AI task from clinical decision support. The model size and quantization profile that works well for note summarization may run unacceptably slowly on the budget Android devices your field staff carry. Getting this wrong means 3 months optimizing a model that produces output your aides won't use because it takes 45 seconds per note.
Connectivity model. Fully offline means no sync until the aide reaches WiFi. Sync-on-WiFi means data integrity depends on when connectivity returns. Background sync means the app manages partial state between sessions. Each has a different HIPAA audit implication. The decision can't be deferred to engineering - it requires compliance input before a line of code is written.
Device coverage. Home health agencies run Android-heavy fleets on budget hardware. A model that performs on a Pixel 6 may not perform on a Samsung A32 or an older Motorola G-series device. Scoping to the device floor you actually have, not the device floor you wish you had, avoids a hardware refresh becoming a dependency for the AI feature.
PHI boundary across modalities. Field staff capture voice, photos, and typed notes. Each modality has a different HIPAA surface area when processed locally. Voice transcription of a visit note is a different PHI handling question than photo capture of a wound. The boundary definition has to be documented before the compliance review, not discovered during it.
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 is Wednesday the right team for on-device AI?
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 long does the integration take, and what does it cost?
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
Is on-device AI right for your organization?
Worth 30 minutes? We'll walk you through what your version of the four decisions looks like, what a realistic scope and timeline would be for your app, and what your compliance posture and 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 a home health mobile app use AI without violating HIPAA?
Yes. If inference runs on-device and PHI never transmits to an external server, there is no cloud processing covered under HIPAA's Business Associate rules. The compliance posture depends entirely on where data flows — Wednesday resolves this in week one.
Q: What is the HIPAA risk of cloud AI vs. on-device AI in a clinical app?
Cloud AI sends every prompt — including any PHI in a note or query — to a third-party server. That server becomes a Business Associate requiring a BAA, which many Privacy Officers won't sign for consumer cloud providers. On-device AI processes locally. Nothing leaves. No BAA required for the inference step.
Q: How long does HIPAA-compliant on-device AI take to ship for a home health app?
4–6 weeks. Week 1: model selection, platform sequence, server boundary, audit trail format. Weeks 2–3: model ships into app behind a feature flag. Weeks 4–5: performance and compliance benchmarks. Week 6: OS coverage, store submission, compliance review readiness.
Q: What does HIPAA-compliant on-device AI cost?
$20K–$30K across four fixed-price sprints: Discovery ($5K), Integration ($5K–$10K), Optimization ($5K–$10K), Production hardening ($5K). Money back if benchmarks aren't met.
Q: Which on-device AI models are appropriate for clinical use?
Documentation assistance: 2B–7B parameter quantized model (Mistral, Gemma, Phi). Decision support: larger model or RAG architecture. Triage screening: under 1B parameters. Model selection is the first decision in the discovery sprint — before any code.
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