Language Parity Test
Do your product and bot work as well in every language as in English?
The job
Measures and closes the gap between how the same product and AI agent work in English and in the customer's own language, feature by feature, with a verdict after each fix.
The moment
The screen shows the product and its AI agent as mirrors: English on one side, the customer's own language on the other. Where they differ, the mirror cracks.
The reporting feature cracks: admins using their own language file 3.4 times as many tickets per user, and 60% say exports garble their names.
The AI agent's mirror cracks too. It resolves 71% of English questions about exports, but only 29% of the same questions in the other language.
Six weeks after the fix, the crack seals under one customer quote: "Finally the names come out right."
What it does
- File a localisation defect with quotes
- Set a parity target in advance
- Request an AI-agent answer update in the customer's language
- Stamp the verdict and send the release scorecard
What you see
Mirrored split with cracks that seal when parity is reached; a second mirror for the AI agent
What it moves
The ratio of non-English to English complaints for each product area, and the gap in questions the AI resolves for each request type, brought down to parity (a ratio of 1.0 to 1.2), with a verdict per release.
Built for
- Product & digital
- Frontline staff
