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
Configuring an intelligence and authoring platform to a corporate investigations firm's subject-check workflow, automating retrieval, triage, translation, and drafting while keeping every claim traceable to its source.
Analysts spent the bulk of a subject check on mechanical work: running the same searches across registries, litigation records, and news archives, translating non-English results, triaging what was relevant, and reformatting findings into the house report template.
A configured deployment that automates retrieval, screening, and drafting beneath the researcher, built on three design commitments established in discovery: every claim traceable to source, comprehensive coverage with a clean fallback to manual work, and the researcher in control of all final output.
Engagement
Design Standard
Serving as embedded Entrepreneur-in-Residence to a grant-funded university research program developing textile-based biosensors, covering FDA regulatory pathway, business model, and capital formation across a 24-month engagement.
A university team had a genuinely novel sensing technology and a 24-month grant, but no regulatory pathway, no business model, and no clear route from a research prototype to a product anyone could buy or a company anyone would fund.
An embedded Entrepreneur-in-Residence role covering FDA regulatory strategy from wellness-device clearance through a software-as-a-medical-device transition, alongside customer discovery, business model development, and funding roadmap.
Our Role
Engagement
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
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
Technical due diligence for a private equity investment in an AI-powered healthcare technology company, assessing engineering capability, product strategy, and how much of the AI story held up under examination.
Assess whether an AI-powered healthcare technology company's technical capability, product roadmap, and organization justified a significant investment, and whether its AI claims reflected genuine capability or repackaged third-party tooling.
Independent technical assessment covering AI and ML capability, platform architecture and scalability, security posture, acquisition integration, data maturity, and the strength of the product and engineering organization.
Engagement
Focus
AI strategy advisory for a private equity firm's portfolio companies, working across industries to identify where AI would pay off and what it would take to implement.
Help portfolio companies across engineering, healthcare technology, food processing, and fitness identify where AI could address real operational problems, and separate those opportunities from the ones not worth pursuing.
A workshop-based advisory process moving each company from problem identification through solution design to an implementation roadmap with architecture, data requirements, and cost estimates.
Engagement
Industries
Commercialization, regulatory, and fundraising strategy for an academic research group taking a validated clinical diagnostic technology from published research toward market entry.
An academic group with substantial publications and clinical validation needed to turn its diagnostic technology into a company: a market entry path, a regulatory route, the right industry partners, and materials credible enough to raise on.
A strategic engagement covering market and competitive analysis, go-to-market design, FDA pathway planning, partner identification, and preparation of investor materials and financial models.
Engagement
Regulatory
