Real-world examples of how we've helped organizations achieve their AI goals while navigating complex regulatory requirements.
AI architecture and evaluation strategy for a multi-year program at one of the world's largest law firms, building a unified workspace for diligence, document negotiation, communications, and institutional knowledge across transactional matters.
Deal execution ran across email, spreadsheets, and systems that did not talk to each other, leaving no unified view of matter status, no structured record of decision rationale, and no reliable way to judge whether an AI system was fit to run on live matters.
Led the AI architecture workstream, directing a team of ten to design a modular, self-hostable platform inside the firm's own cloud tenant, and established the benchmarking discipline used to decide what shipped.
Our Role
Team Led
Building an AI agent that converts benefits requirement workbooks into platform-ready configuration, replacing a manual translation step with a reviewable, confidence-scored workflow.
Onboarding each new employer group required implementation teams to hand-translate dense, inconsistently formatted requirement workbooks into platform configuration: slow work that did not scale with the sales pipeline, and a recurring source of configuration errors that surfaced later as member-facing problems.
Delivered an AI agent that extracts and maps configuration data from requirement workbooks into platform-ready artifacts, wrapped in a review interface with per-field confidence scoring and source traceability on every mapping.
Onboarding Time Saved
Field-Level Accuracy
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
