Case Studies
How we validate AI systems for production across regulated and high-stakes industries. Each engagement leaves the team with the test suites and datasets to keep.
Representative engagement. Client details are anonymized and figures are illustrative ranges shown to convey the type and breadth of work, not a specific named result.
FintechSeries B Fintech
An LLM support assistant gave confident but wrong answers about fees and account rules. A readiness audit, a golden dataset built from real transcripts, and CI gates brought the error rate under control before it reached more users.
AI StartupsSeries A AI SaaS
A multi-step agent that took real actions caused incidents when it called the wrong tool or stepped outside its scope. Adversarial agent testing and a regression suite in the repo turned an unpredictable feature into a shippable one.
Healthcare300-bed Hospital Network
An ambient documentation tool risked adding statements a clinician never said. Physician-informed risk mapping, safety and groundedness testing, and human-in-the-loop review made the output safe enough for clinicians to trust.
Retail & E-CommerceMid-market E-commerce
An AI search and recommendation layer returned irrelevant results for some queries and skewed results for some customer segments. Golden query sets and segment-aware evaluation made quality measurable and repeatable.
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