Short answer: Defense 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 program office approved an AI feature for the field operations app. Your AO rejected the ATO extension because the AI inference runs on a commercial cloud API outside the authorization boundary. The field team is still working without it.
ATO rejections for AI features are now the most common government mobile program delay. The fix is architectural, not administrative.
The Four Decisions That Determine Whether This Works
Authorization boundary definition. The ATO covers systems within a defined boundary. A commercial AI API outside that boundary requires either a separate ATO for the API provider or a FedRAMP-authorized cloud alternative. On-device inference eliminates the external dependency and keeps the AI within the existing authorization boundary. This is the structural answer to the AO's objection, not a workaround.
IL classification of model inputs. If the data the model processes is classified or CUI, the model and its runtime have to operate within the appropriate enclave. On-device processing on a government-issued device with MDM controls keeps the data within the device boundary. The MDM configuration and device classification determine what's permissible — scoping this before the integration sprint avoids a compliance finding during the security review.
Open source model vetting. Government programs using AI models need to be able to explain what the model was trained on and whether it introduces supply chain risk. Open-source models with documented training sets and known provenance are more defensible than proprietary commercial models in a government ATO context. The model vetting documentation becomes part of the ATO package.
Update and patch cadence. An on-device model on a government device needs to follow your patch management cadence. Model updates have to go through the same change control process as software updates, which means planning the update deployment mechanism as part of the ATO package. A model that can't be patched through your existing change control process is a compliance liability from day one.
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 ATO Pathway?
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 defense 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 defense 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 defense 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 defense 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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