Short answer: Aviation field teams can run AI-powered inspection, documentation, and reporting offline — no cell coverage required. Wednesday ships these integrations in 4–6 weeks, $20K–$30K, money back.
Your line maintenance technicians work on the ramp and in hangars with no WiFi coverage. Your AI task card assistance and defect logging features require connectivity that doesn't exist where your mechanics actually work.
A maintenance tool that requires WiFi is a tool that gets abandoned at the start of a shift. Mechanics who can't get task card assistance in the hangar will either memorize or skip the AI step. Both outcomes reduce the value of a tool you've already invested in building.
What decisions determine whether this project ships in 6 weeks or 18 months?
Four decisions determine whether your maintenance AI becomes part of the mechanic's standard workflow or a feature that lives in the office app and not the hangar.
Regulatory documentation requirements. Aviation maintenance records are regulatory artifacts. An AI that assists with documentation has to produce outputs in the format your Part 145 organization requires - specific task card structure, labor code references, part number format, and authorized signature workflow. A model that produces free-form text summaries that mechanics then have to reformat for the technical log is not a documentation tool. It's a draft generator that adds a step. The output format contract between the AI and your technical record system has to be defined before the model is configured.
AMM and SRM reference lookup. Mechanics need to look up Aircraft Maintenance Manual procedures and Structural Repair Manual sections during a task - often without moving away from the aircraft. A local RAG model over a locally cached AMM delivers this without connectivity. The constraint is data licensing. OEM manual licensing terms - Boeing, Airbus, and others - typically restrict redistribution and caching. Your legal team needs to confirm the caching rights under your current manual subscription before the offline lookup architecture is built, or you'll discover the licensing problem after the feature is already built.
Offline voice capture in high-noise environments. Ramp and hangar environments run at 85-100 dB during operations. A voice transcription model tested in office conditions will produce high error rates on the inputs it actually receives in the field. The transcription model has to be evaluated and fine-tuned against recordings taken in actual maintenance environments - engine run-up conditions, pneumatic tool noise, hangar PA systems - before you commit to voice as the primary input method.
Integration with your MRO system. Task completions, parts requests, and defect reports that AI assists with have to flow back to your MRO system - AMOS, RAMCO, Trax, or your equivalent - with no manual re-entry. Every data element the AI captures has to map to a field in your MRO system's data model. That mapping exercise has to happen in the discovery sprint, because discovering a mapping gap in the integration sprint means rebuilding the data model mid-project.
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.
"I'm most impressed with their desire to exceed expectations rather than just follow orders." - Gandharva Kumar, Director of Engineering, Rapido
Is on-device AI right for your organization?
Worth 30 minutes? We'll walk you through what your field workflow and connectivity constraints mean for the project shape, and what a realistic scope looks like.
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 aviation field teams use AI without cell coverage?
Yes. On-device AI runs the model locally on the device's Neural Engine. No network request is made during inference. A field inspector in a dead zone gets the same AI capability as one with full LTE. Data syncs when connectivity returns.
Q: What AI tasks can run offline on a aviation field app?
Inspection checklist guidance, defect classification from photos, report drafting from voice or structured input, procedure lookup, equipment identification, and compliance documentation. Tasks requiring real-time external data — live pricing, inventory lookup — still need connectivity.
Q: How long does offline AI for a aviation field app take?
4–6 weeks. Week 1: model selection, connectivity boundary, sync conflict architecture. Weeks 2–3: model ships into app. Weeks 4–5: performance on minimum device spec. Week 6: store submission.
Q: What does offline AI for a aviation field app cost?
$20K–$30K across four fixed-price sprints, money back if benchmarks aren't met.
Q: What device spec is required for on-device AI on a field app?
iPhone 12+ (2020) and Android with Snapdragon 8 Gen 1+ (2022) run quantized 2B–7B models at acceptable latency. Older devices may need a smaller model or cloud fallback. Minimum spec is assessed in the discovery sprint.
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