A voice or chat model becomes unreliable long before its dashboard makes the problem obvious. Strawberry can inspect the Wit.ai workspace you have open, compare intents, utterances, entities, and training changes, and prepare a test plan or defect brief that makes the next decision easier.
01
Test the model against the conversations it is meant to understand.
A list of intents is not proof that people can use them naturally.
Strawberry can inspect the examples and the real conversations you provide, then prepare a coverage review that identifies ambiguous phrasing, thin training sets, and the cases worth testing first.
02
Separate a training suggestion from a training change.
A proposed utterance, entity value, or intent split can be sensible and still be wrong for the product. Strawberry can collect the supporting evidence into a reviewable recommendation, so the person responsible for the model can test the reasoning before anything changes.
03
Make regression checks part of the release conversation.
When product language changes, the assistant can quietly stop recognising an important request. A companion can turn release notes, open browser tabs, and the visible Wit.ai setup into a targeted regression checklist for the next QA pass.
04
Keep the same quality bar when the workspace grows.
Every two weeks, the Wit.ai QA pass can compare newly added utterances against ambiguous intents, missing entities, and the regression tests the app needs before the next training decision. It must not alter training data or publish a model: an unreviewed edit can teach the wrong intent and route real voice or chat requests to the wrong experience.
Wit.ai Strawberry