Real-world examples of how we've helped organizations achieve their AI goals while navigating complex regulatory requirements.
Architecting and delivering an AI documentation agent embedded in a mobile clinical application, converting surgeons' voice dictation into structured operative notes ready for billing.
Surgeons dictated operative notes after every procedure, but the audio was sent out for human transcription, adding up to 72 hours before documentation was final and billable and leaving physicians to catch transcription errors after the fact.
Designed a decoupled, stateless AI engine embedded in the client's existing mobile web application, providing real-time transcription, template structuring, and voice-driven editing on HIPAA-compliant infrastructure.
Our Role
Team Led
Designing and implementing an automated, reproducible data and machine learning pipeline for a research program at a leading academic medical center, whose models carry peer-reviewed validation and a path to clinical deployment.
Strong deep learning models were being trained on a fragile foundation: hand-run scripts, cron jobs, and one-off statistical procedures spread across personal machines, with no lineage, no reproducibility, and silent failures whenever a team member was away.
Designed and led implementation of an automated, version-controlled pipeline, containerizing the team's existing statistical tooling, orchestrating it under a modern scheduler, and consolidating all code into a single authoritative repository with CI/CD.
Our Role
Team Led
