AI-Powered App Development

AI Features in Mobile Apps

Add AI that carries its weight: an assistant that knows your content, smart search, personalised feeds, camera features, and voice input, each built on-device or in the cloud depending on what it needs.

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AI in an app is easy to get wrong

Tacked-on AI features tend to be gimmicks that nobody uses, or they quietly drain battery and mobile data. The features that stick are the ones that remove a real tap or a real wait. Getting there means choosing carefully, deciding per feature whether it runs on the device or in the cloud, and being honest about latency, cost, and what happens with no signal.

What we build

  • An in-app assistant or chat grounded in your own content and data
  • Smart search and filtering that understands intent, not just exact words
  • Personalised feeds and recommendations driven by your data
  • Camera and vision features (scan, classify, extract) where they save a step
  • Voice input and dictation for hands-free flows
  • On-device models for offline use and privacy-sensitive data, with a cloud fallback

How the build runs

  1. 1

    Shortlist features

    We list candidate AI features and keep only the ones that remove a real tap or wait for your users.

  2. 2

    On-device or cloud

    Each feature is assigned to run on the device or call a cloud model, based on latency, cost, and privacy.

  3. 3

    Build and integrate

    Features are built into the app with proper loading, empty, and error states.

  4. 4

    Test on real devices

    We test on low-end phones and throttled networks, not just a flagship on wifi.

  5. 5

    Ship with limits

    Usage limits, cost monitoring, and a no-signal fallback are in place before release.

What you get out of it

  • AI features people actually use, not demo-ware
  • Predictable battery, data, and cloud cost
  • Sensible behaviour when the network or model is unavailable
  • A clear read on which features earn their place

Questions we get about this

Some will. Smaller models run on the device and work with no connection. Features that need a larger model call the cloud and require a signal, with a defined fallback when there is none.

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