Chai & Kettle
A deep-learning NLU engine for voice actions — ~99% accuracy from a handful of examples, shipped to 3M+ devices.
Reverie · Low-data NLU for voice actions
Overview
A leading set-top-box provider wanted to run actions from voice. The speech team handled the ASR; we built the part that decides what you actually meant.
At Reverie, a former colleague — and dear friend — proposed we build the NLU engine together. We did, in marathon mode: the model, and the whole platform around it.
01 — The split
From voice to action
Speech becomes text (ASR), text becomes intent (NLU), intent becomes an action on the device. The speech team handled ASR — on-device and cloud. We built the NLU layer — the part that turns 'put on the cricket' into a command the box can run.
02 — The model
Intent from almost no data
The hard constraint: recognise intent from a handful of examples, in any language. This was before transformers reshaped NLU — so the toolkit was multilingual word and sentence embeddings (LASER's BiLSTM encoder, fastText subwords, MUSE's aligned vector spaces) over a BiLSTM / CNN classifier, with BPE keeping it language-agnostic. We built a deep-learning engine on those ideas — handling multi-intent, with auto-annotation to stretch every example further.
- LASER
- fastText
- MUSE
- BPE
- BiLSTM
- CNN
03 — The platform
Create a class, click train, ship
Chai was the backend brain; Kettle the platform on top. Create a project, add intent classes, add a few examples, click to train, deploy, and test the new model — a no-code loop a non-ML team could run themselves. Teach-me / test-me, auto-annotate, repeat.
04 — Operations
Zero-downtime by design
New models shipped constantly, so we built the AutoML ops to swap them in with zero downtime — traffic never dropped while a freshly trained model took over. Retraining stopped being an event and became routine.
Outcome
~99%
Accuracy across all set-top-box use cases
3M+
Devices across India
Almost no data in. Ninety-nine percent out — across three million devices.
Role
Built the NLU engine and the Chai & Kettle platform with a longtime collaborator — model, training loop, and the AutoML ops that let new models ship with zero downtime. Delivered well past target and into millions of living rooms.