Your telehealth app serves patients in rural areas where cellular coverage is 2G at best. Your AI triage and symptom assessment features require a data connection your patients don't have when they're most likely to need care.
The patients with the worst connectivity are often the patients with the fewest alternatives. An app that fails them offline fails them at the worst possible moment.
The Four Decisions That Determine Whether This Works
Triage vs symptom checking vs documentation. Triage decision support — should this patient seek emergency care now — has a different risk profile than symptom checking and documentation assistance. Starting with triage adds the most clinical value and has the clearest validation pathway. A model that prompts escalation to emergency care has a defined correct-answer standard. A model that generates symptom explanations requires more open-ended validation.
Connectivity detection and graceful degradation. The app should detect when connectivity is insufficient for cloud features and switch to on-device mode automatically. A patient who doesn't know they're offline and gets a silent failure at triage is in a worse position than a patient who gets explicit guidance from an on-device model. The degradation logic is a clinical design decision, not just an engineering one.
Language and literacy. Rural and low-connectivity populations are linguistically diverse and may have varying health literacy. The on-device model needs to be tested on inputs in the relevant local languages and at varying literacy levels, not just on standard clinical English. A triage feature that works for an educated English speaker and fails for a Tamil-speaking rural patient is not a solution for your user base.
Clinical content currency. An on-device clinical model needs to be updated when clinical guidelines change. The update deployment mechanism has to work on low-bandwidth connections — small delta updates, not full model redownloads. A model that can't be updated is a clinical liability within 12 months of deployment.
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.
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.
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