AI-Powered Mobile Keyboard
On-device language intelligence for fast, private, multilingual typing.
The brief
Typing experiences in multilingual markets are hard: users switch scripts mid-sentence, transliterate freely, and expect instant suggestions. Cloud prediction was a non-starter for both latency and privacy.
The client wanted a keyboard whose intelligence ran entirely on the device: fast, private, and genuinely good at the mixed-language way people actually type.
- Deliver instant prediction and autocorrect suggestions on the phone.
- Run entirely on-device. No text may ever leave the phone.
- Handle multilingual, script-switching and transliterated typing naturally.
- Personalise to each user locally, with no central data collection.
- Adapt to each user's typing style over time.
- Improve continuously for each user without compromising privacy.
Our AI-native approach
We built compact language models with TensorFlow Lite, quantised to run within a strict latency and memory budget on the device. The challenge was less raw modelling and more disciplined engineering: every millisecond and megabyte was measured.
Privacy was a design constraint, not a feature. Text never leaves the device, and personalisation happens locally.
What we built
On-device prediction
Next-word and correction models run locally with no network round-trip.
Multilingual & transliteration
Handles script-switching and romanised input the way users really type.
Local personalisation
The keyboard adapts to the user's vocabulary without sending it anywhere.
Smart autocorrect
Context-aware correction that respects mixed-language sentences.
Tiny footprint
Quantised models tuned to load fast and stay within memory budgets.
Privacy by default
No keystroke leaves the device; personalisation is fully local.
How we built it
This was a discipline problem more than a modelling one, so we built against a hard latency and memory budget from the first commit and measured every change against it. Models were quantised and pruned specifically for the target hardware tiers rather than shipped at full size.
We prototyped the transliteration path early, because it was the riskiest part of making typing feel natural, then hardened it. We owned on-device inference and the iOS and Android integrations, and we validated that personalisation happened entirely locally so no keystroke ever left the handset.
How it works
- Step 01
Capture
Keystrokes are processed entirely on device.
- Step 02
Predict
Compact TF Lite models score next-word and corrections.
- Step 03
Adapt
A local profile personalises suggestions over time.
- Step 04
Render
Suggestions surface within the latency budget.
- Step 05
Forget
No raw text is stored or transmitted off device.
The language models are quantised and pruned specifically for the target hardware, trading a sliver of accuracy for the responsiveness a keyboard demands. Transliteration is handled as a first-class path rather than bolted on, which is what makes mixed-script typing feel natural.
All learning is on-device, so the product improves for each user without any central data collection.
The impact
Suggestions arrive instantly because inference runs directly on the phone.
Mixed-language and transliterated typing worked the way users expect.
Privacy held as an architectural guarantee, not a policy promise.
Typing adapts to each user's style over time, across multiple languages.
Technology stack
Related service: Web & Mobile App Development.
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