Short answer: Maritime 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 vessel crew is at sea for 14-day legs with VSAT connectivity that costs $8 per megabyte. Your fleet management app was designed for port WiFi.
An app designed for port WiFi is an app that doesn't work at sea. At $8 per megabyte, every round-trip AI API call is a cost your operations team can see on the satellite invoice. At 14 days of intermittent VSAT, an AI feature that depends on cloud inference is an AI feature that's unavailable for most of the voyage.
What decisions determine whether this project ships in 6 weeks or 18 months?
Four decisions determine whether your fleet AI delivers value on the water or only in port.
Bandwidth-constrained vs offline. VSAT at sea isn't offline - it's expensive and unreliable enough that it behaves like offline for AI features that make full-token cloud API calls. On-device processing eliminates the VSAT cost for AI features entirely. A crew member who gets an immediate defect classification result from an on-device model doesn't need to wait for a VSAT window or manage a satellite data budget. This changes the economics of shipping the AI feature: a feature that costs $40 in VSAT fees per crew member per voyage is a feature that never gets used. A feature that runs locally costs nothing after deployment.
Hazardous environment hardware. Maritime devices are often ruggedized Android tablets certified for use in classified zones - ATEX Zone 2, IECEX, or equivalent. These devices run older Android versions, have limited storage, and have NPUs that lag 2-3 generations behind consumer flagship hardware. The model has to run within acceptable latency and within the storage budget of whatever hardware your fleet currently operates. Scoping to your actual device inventory, not a reference device, avoids discovering the performance gap after the first vessel deployment.
Multi-language support. International crewing means apps need to work in multiple languages. A vessel with Filipino officers and Indonesian ratings and a German operator requires documentation AI that works in at least those three languages. On-device language model support for Filipino, Indonesian, and English is a different scope from single-language support. The language coverage plan has to be part of the initial build, not a follow-on sprint that ships 6 months after the original feature.
Offline documentation integrity. Cargo documentation, safety inspection records, and maintenance logs created during a 14-day voyage need a verifiable chain of custody when they sync on port arrival. Maritime regulatory audits - SOLAS, ISM Code, MLC 2006 - require that records be tamper-evident and traceable to the crew member who created them. The integrity mechanism for offline records has to be built into the sync architecture, not added as a compliance retrofit after the first port state control inspection.
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 maritime 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 maritime 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 maritime 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 maritime 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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