Services / AI app development
AI app development: generative and intelligent features for mobile
AI features are now product features: they need the same design, engineering and economics as everything else in the app. We build generation, search and assistant features that ship, that users understand, and that pay for their own inference.
Our own AI apps are live in the App Store, so the questions we ask are practical. What does a generation cost? How long will the user wait? What happens when the model produces something it should not? How is user data handled and disclosed?
We work with hosted models from OpenAI, Anthropic and Google, open models where privacy or cost demands it, and on-device models with Core ML and ML Kit when latency or offline use is the requirement.
What is included
AI app development, end to end
- ■AI feature discovery: where a model adds value and what it costs
- ■Image, text and audio generation pipelines
- ■Semantic search and retrieval over your content
- ■In-app assistants with tool use and guardrails
- ■On-device inference with Core ML, ML Kit and small models
- ■Prompt, evaluation and regression testing
- ■Rate limits, caching and cost controls per user
- ■Moderation, safety and App Store AI policy compliance
- ■Usage analytics tied to subscription economics
Who it is for
- □Products where generation or search is the core experience
- □Existing apps adding an AI feature without breaking the business model
- □Teams that need the cost and safety work done, not just a demo
Technologies
Process
How the work runs
- 01
Discover
We define the product, its users and the business case, audit any existing code or designs, and agree the scope, platform strategy and success metrics for the first release.
- 02
Design
User flows, wireframes and a high-fidelity prototype: clickable for apps, playable for games. We test it with real users before production starts, so the build begins from validated screens and a core loop that is already fun.
- 03
Build
Native or cross-platform, decided in Discover. Two-week sprints, a TestFlight or Play build at the end of each, automated tests and a CI pipeline from the first week.
- 04
Launch and grow
Store listing, screenshots, review submission and release. After launch we run analytics, A/B tests on onboarding and paywalls, and a roadmap for the next versions.
Proof
Products we build and publish ourselves
The same team and process behind every client engagement.
See your next manicure on your own hands, then design, fix and plan it in one place.
Is your match real? Find out who someone is before you meet.
See any haircut, colour or style on your own face before you cut it.
Try any piercing on a selfie, without the needle or the second-guessing.
FAQ
AI app development questions
How do you keep AI inference costs under control?
By design: the right model for each task, caching, batching, per-user limits tied to the subscription tier, and usage analytics that show cost per feature. We model the economics in Discover before committing to a feature.
Which models do you use?
The one that fits the task. Hosted models from OpenAI, Anthropic and Google for quality and speed of iteration, open models where privacy or cost requires it, and on-device models when latency or offline use matters.
How do you handle safety and moderation?
Input and output moderation, prompt guardrails, human-review paths for flagged content and clear user-facing rules. App Store and Google Play both have AI content policies, and compliance is part of the release plan.
Can AI features run on the device?
Increasingly, yes. Core ML and ML Kit run vision, speech and small language models locally, which removes inference cost and improves privacy. We use on-device models wherever they meet the quality bar.
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