Real-world examples of how we've helped organizations achieve their AI goals while navigating complex regulatory requirements.
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
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
