Short answer: Government organizations can deploy AI in mobile apps with zero cloud dependency — the model runs entirely on the device's local processor. No network required at inference time. Wednesday ships these in 4–6 weeks, fixed price.
Your classified mobile program needs AI inference capability with no external network dependency. Your program office has evaluated three commercial solutions and rejected all of them because every option requires connectivity to a commercial API.
The connectivity requirement is not an implementation detail. It is a disqualifying architectural property for classified environments.
The Four Decisions That Determine Whether This Works
Hardware security requirements. Classified mobile applications typically run on NSA-approved or DoD-certified devices. The on-device AI model has to run within the hardware security architecture those devices impose — trusted execution environments, secure enclaves, and hardware attestation requirements that general-purpose AI frameworks don't natively support. Selecting a model format that works within these constraints is the first scoping decision.
Model supply chain. The model provenance question is more stringent in classified environments. The training data, the training organization, and the model weights all require vetting before deployment. An open-source model from a foreign research institution is a different risk profile from a domestically trained model with audited training data. The supply chain documentation has to be ready before the model goes into an ATO package.
Air-gapped model update mechanism. Classified devices don't reach the public internet. Model updates have to be delivered through your classified network's software distribution mechanism, packaged the same way as OS updates and application patches, and signed with certificates your PKI infrastructure recognizes. A model that can't be updated through your existing classified distribution channel is a model that becomes a liability when the next vulnerability is found.
Audit and accountability. Classified AI applications require audit logging that satisfies your security classification guide. Every model invocation, every input, and every output needs a log entry at the classification level of the application. The logging architecture has to be designed for your classification guide, not for a commercial app store review. Retrofitting audit logging after deployment is significantly more expensive than designing it in.
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 the Classified AI Architecture?
Worth 30 minutes? We'll walk you through what your security posture, your deployment environment, and your compliance requirements mean for the project shape.
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 government mobile apps use AI in air-gapped or EMCON environments?
Yes. On-device AI requires no network connectivity at inference time. The model is loaded during provisioning. In air-gapped environments, model updates are distributed through the same provisioning channel as OS updates.
Q: What FedRAMP authorization is required for on-device AI in government apps?
On-device AI that doesn't transmit data to a cloud service falls outside FedRAMP scope for the AI component. The app infrastructure — authentication, data sync, backend APIs — still requires appropriate authorization. The architecture decision about what leaves the device determines what falls inside FedRAMP scope.
Q: How long does on-device AI for a government mobile app take?
4–6 weeks for technical integration. Compliance documentation and ATO process varies by agency and classification level. Wednesday delivers a 1-page architecture doc in week one your security team can use to initiate the ATO process.
Q: What does on-device AI for a government mobile app cost?
$20K–$30K across four fixed-price sprints, money back if benchmarks aren't met.
Q: Can on-device AI models be updated without connecting to the internet?
Yes. Model updates are distributed as binary assets through the secure software distribution channel — the same infrastructure used for app updates in classified environments.
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