Our work
Three systems where the AI had to be trustworthy
Every project below solves the same underlying problem: making a language model behave predictably enough to be trusted inside a real business process. The numbers come from delivery reports, load tests and the code itself.
An AI tutor that refuses to make things up
Smart Clinical Tutor, built for Mega Summit (U) Ltd. Every citation is validated against the text actually retrieved. A fabricated citation causes the answer to be rejected rather than shown, and thin evidence returns "insufficient evidence" instead of a guess. Load tested to six times its target with no crash. Live and serving universities and individual students from the same system.
Four milestones in eight days, and one rescue
CruCare Hub, built for CruCare Health in Metro Detroit. The AI gives the same answer twice, safety rules live in code rather than prompts, and doses come verbatim from the catalogue. Delivered in eight days against a fourteen-day scope. When the model vendor retired the version it depended on, we restored every consultation and shipped a safeguard against it happening again.
What we build when the client is us
Our own order-management platform: 70 data models, 221 API routes, a Vue web app, a React Native mobile app, three courier integrations and server-side ad tracking that reports real delivery outcomes rather than clicks. Proof of range, on a product we own.
Have a model that needs to behave?
We build AI systems that are checkable: grounded answers, reproducible output, safety rules in code, and tests that gate every deploy. Tell us what is breaking and we will tell you whether we can fix it.
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