Lami
Decoding human-AI interactions
AI product teams struggle when domain expertise gets trapped in silos. Business goals, product requirements, and compliance rules stay fragmented in documents, disconnected from AI workflows, leading to frustrating delays and guesswork.
Think of it as a vibe evaling tool for domain experts, Lami’s AI engine lets people encode and continuously update their product requirements directly into thier product development workflow for a better collaboration with engineers. Every AI product change is traceable and tied to business signals, so teams can ship with confidence.
User feedback like thumbs up/down, edits, or regenerations is just noise if you can’t act on it. Lami turns that noise into a clear, actionable signal by transforming messy interactions into structured and traceable feedback.
The semantic layer cuts through the noise by turning messy AI outputs—like freeform text or ambiguous behavior—into structured, interpretable signals teams can trust. It sits between raw model behavior and human evaluation, translating subjective reactions into consistent, repeatable judgments. This moves evaluation beyond vague vibe checks to something precise and actionable.
Instead of debating whether something “feels“ off, teams can pinpoint why exactly an output deviates, grounded in business logic and domain specific rubrics. The result is a feedback loop that not only monitors performance but also drives meaningful improvement.
Research & Design
JJ Moi
