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
Standing up the end-to-end data and machine learning pipeline for a non-invasive biosensing startup, taking a hardware founder from raw sensor output to a reproducible training and evaluation system.
A medtech founder had working sensor hardware, accumulating time-series data, and no machine learning infrastructure: no reproducible path from raw sensor output to a trained model, and no way to tell whether a promising result was real signal or an artifact of fitting too many features to too little data.
Built an end-to-end training and evaluation pipeline spanning ingestion, feature engineering, time aggregation, and cross-validation, then used it to run a structured experimental program across target formulations, feature methods, and model architectures.
Our Role
Team
