Short answer: Intelligence 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 tactical field app requires AI inference capability in environments with no radio frequency emissions permitted and no commercial network access. Every commercial AI solution your program evaluated requires external connectivity your operational environment cannot allow.
Connectivity-dependent AI is not a risk-managed option for EMCON-compliant operations. It is a disqualifying property.
The Four Decisions That Determine Whether This Works
Operational environment constraints. Tactical field AI operates under emission control requirements that prohibit RF transmissions in certain scenarios. The AI architecture has to be documented as non-transmitting in the applicable EMCON condition. On-device inference with all network interfaces disabled satisfies this requirement. An edge server in the operational area does not, unless it operates on a classified network segment. The architecture documentation has to make this distinction explicit for the AO review.
Platform and hardware certification. Tactical mobile devices are typically ruggedized platforms with specific OS versions and security configurations — for example, Android-based platforms with NSA IA controls. The AI runtime and model format have to be compatible with the security configuration of the approved platform, not with mainline Android. Testing against the approved platform spec, not a development device, is a project requirement.
Sensor fusion inputs. Tactical AI often needs to fuse inputs from multiple sensors: camera, GPS, radio intercept, and human intelligence reports. The model architecture has to handle multi-modal inputs on a device with constrained compute budget. Scoping which sensor inputs the model actually needs for the mission reduces compute requirements and implementation risk — not every sensor feed needs to be an AI input to deliver mission value.
Operational data handling. Data processed by tactical AI during an operation may be classified at a level that requires specific handling procedures for export, storage, and destruction. The data handling architecture has to be designed for the classification guide before the model is deployed. Retrofitting classification-compliant data handling after deployment is a program reset, not a patch.
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 Tactical 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 intelligence 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 intelligence 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 intelligence 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 intelligence 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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