AI systems where every answer is measured, every failure is traced and every release is tested.
An AI Demo Works Once. AI in Production Has to Prove Itself Every Day.
Most AI projects stop at "we tried it and it looks fine". We treat AI like any other software: we test it, measure it and catch regressions. We test our voice assistants by putting them in conversation with another AI playing the customer; the conversation's event timeline is verified automatically against written scenarios.
We don't treat latency as one number. We measure a sentence's mouth-to-ear journey step by step and know where every second goes. Privacy is a test, not a claim: every release measures, with the same question set, whether the model leaks personal data. Every call is traced end to end, and cost and latency targets are part of the scenario tests.
What We Build
- LLM-simulated customer tests: agents talk to synthetic customers before real users
- End-to-end latency telemetry with backend, model and avatar shares split
- Privacy and leakage evals: role-based question sets on every release
- Scenario tests with cost and latency targets
- Per-session end-to-end traces and error tracking in Langfuse
Where It Fits
Any customer-facing AI product, AI in regulated sectors, and teams taking AI from pilot to production.
In Our Own Projects
Hardiye's speech-to-speech test harness and latency breakdown, Motice's leakage measurement on every release, and APEX's cost-capped scenario tests.