Medical diagnostics & clinical-adjacent AI
Below is our project for clients in medical diagnostics and clinical-adjacent AI— not a generic template. We paired careful UX with local LLMs so sensitive workloads stay on infrastructure the customer controls.
What we built for this industry
The agent we built
A clinical diagnostic co-pilot agent— orchestrated by our stack—that walks specialists through structured, multi-turn sessions. It ingests case notes and imaging-adjacent context the client approves, proposes ranked differentials and next-step prompts, and attaches rationale so teams can audit what the model weighed. Every substantive output routes through human-in-the-loop checkpoints; the agent never issues a final diagnosis on its own. Inference runs only against local LLMs the institution hosts, with our layer handling session state, policy gates, and immutable logs for compliance review.
That agent sits inside a broader assistant experience aimed at decision support—never a substitute for professional judgment or institutional protocol. We ran inference through on-prem and VPC-hosted local models so rounds, imaging-adjacent notes, and research questions did not need to leave the client's approved perimeter. We also shaped flows so outputs could be reviewed, logged, and governed the way their compliance stakeholders expect.
This engagement is representative of how we work: we own the architecture and product surface end-to-end, we stay explicit about boundaries and risk, and we stay ready to extend the same patterns for your environment.