AI · Product
AI app development that earns its latency.
Assistants, personalisation, vision and workflows — built as product features with real failure states, not a demo behind a text box.
Stack
01Scope
What AI product features cover.
The engineering around the model matters more than the model.
- Assistants and natural language
- Streaming responses, tool calling, conversation state and the interruption handling that makes a chat surface usable on a phone.
- Retrieval and vector search
- Embeddings over your own content, so answers are grounded in your data instead of the model's guesses.
- Vision and multimodal
- Camera capture, document and receipt understanding, and image input paths that degrade gracefully on a bad connection.
- Cost and latency control
- Caching, model routing, token budgets and a hard ceiling, so a feature that works in testing does not become the largest line on your bill.
02Product reality
The parts teams skip.
Every one of these is what separates a shipped AI feature from a suspended one.
- Failure states
- Timeouts, refusals, rate limits and wrong answers are normal operating conditions and need designed behaviour, not a spinner.
- App Store review
- Apps with generative content need moderation, age ratings and reporting paths. Reviewers check, and rejections here are slow to resolve.
- Privacy posture
- What leaves the device, what is retained and what you disclose. On-device inference removes the question entirely where it fits.
03FAQ
AI app development: common questions.
Can you add AI features to our existing app?
Yes, and it is the more common engagement. We scope a single feature with a measurable job, build it with real failure handling and a cost ceiling, and ship it before expanding.
Which models do you build on?
Whichever fits the job, cost and privacy constraints — hosted LLM APIs for reasoning and generation, on-device Core ML and Apple's frameworks where the data should never leave the phone. The routing decision is written down, not assumed.
How do you control AI running costs?
Caching, prompt and context budgets, cheaper models for the easy path, and a hard per-user ceiling built in before launch rather than after the first bill.
04Related
Related capabilities.
Most engagements touch more than one of these.
On-device AI
Inference on the phone: no round trip, no server bill per request, and no privacy policy explaining where your users' data went.
iOS app development
Swift and SwiftUI, current-generation Apple frameworks, and a release you own end to end. No wrappers, no WebView shortcuts.
MVP development
One platform, one sharp idea, in front of real users. Scoped hard, built properly, and instrumented well enough to learn something.
Tell us what you're building.
Send the rough version. A senior engineer reads every brief and replies within one business day.