Short answer: On-device AI delivers sub-100ms response times, zero network-call battery overhead, and full offline functionality — because the model runs on the device's Neural Engine, not a remote server. Wednesday ships these integrations in 4–6 weeks, fixed price.
Your app's AI features fail silently when users lose connectivity. Your support ticket volume spikes every time there's a network issue, because users don't know whether the AI failed or whether they did something wrong.
Silent failures erode trust faster than slow features. Users who can't tell what's happening assume the problem is them.
The Four Decisions That Determine Whether This Works
Offline vs degraded vs connected mode. Offline-first architecture has three states, not two. Fully offline — no network at all — degraded — intermittent or slow network — and connected — reliable network — each require different behavior from your AI features. Most apps handle connected and offline but not the degraded middle case, which is where most real-world connectivity problems live. The degraded case is where silent failures happen.
Which features run offline. Not all AI features are worth the engineering cost of offline support. Start with the features your users are most likely to need during connectivity loss. A field service app's inspection AI needs to be offline-capable. Its admin reporting dashboard doesn't. Scoping which features need offline support reduces project cost without reducing the user-facing value.
Sync conflict resolution. Data created by on-device AI during offline periods has to sync to your backend without overwriting data that changed server-side during the same period. The conflict resolution logic has to handle the case where the server and the device have diverged, not just the clean-sync case. Getting this wrong creates data loss that is harder to explain to users than a connectivity error.
User-visible state. Users who don't know the app is offline blame the AI when features behave differently. A clear, unobtrusive indicator of connectivity state — and an explanation of which features are available in each state — reduces support volume and user frustration. Designing this into the app before it ships is cheaper than adding it after the support tickets start.
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.
"They delivered the project within a short period of time and met all our expectations. They've developed a deep sense of caring and curiosity within the team." — Arpit Bansal, Co-Founder & CEO, Cohesyve
Ready to Map Out the Architecture?
Worth 30 minutes? We'll walk you through what your app's current performance profile means for the on-device scope, and what a realistic timeline 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: What response time can on-device AI achieve on a modern smartphone?
Under 100ms first token on iPhone 15 or Pixel 8 with a quantized 2B model. No network round-trip. The latency floor is the Neural Engine speed, not a server queue.
Q: How does on-device AI affect battery life vs. cloud AI?
LTE/5G radio activity is one of the highest battery consumers on a smartphone. Cloud AI triggers a network request for every inference. On-device uses the Neural Engine — power-optimized for matrix operations — with no radio activity.
Q: Does on-device AI work without internet?
Yes. The model is downloaded once and stored on-device. Every inference runs locally. Key for apps used in low-connectivity environments: rural areas, underground, aircraft mode, emerging markets.
Q: How long does on-device AI integration take?
4–6 weeks. Discovery identifies model size for performance targets, minimum device spec, and offline sync architecture.
Q: What does on-device AI integration cost?
$20K–$30K across four fixed-price sprints, money back if benchmarks aren't met.
Top comments (0)