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
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
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
