Case Studies
Anonymized examples of production healthcare systems we've designed, built, and shipped.
Unstructured clinical notes contained critical patient signals that couldn't be queried or analyzed at scale. Manual review was the only option.
Multi-agent LLM pipeline with schema validation, cloud data warehouse integration, confidence scoring, and automated retry logic. Designed for HIPAA-compliant inference with a strict accuracy-over-hallucination priority.
Automated extraction of structured clinical signals from EHR notes at scale, enabling downstream analytics and risk stratification that was previously impossible.
Care coordinators were manually reviewing patient records to identify gaps in care, flag rising-risk members, and route follow-ups. The process was slow, inconsistent, and couldn't scale across a growing member population. The team needed a system that could combine structured claims data with unstructured clinical notes to surface actionable insights without requiring a human in every loop.
Built an agentic orchestration layer that combined deterministic rules (claims triggers, lab value thresholds, care gap logic) with LLM-augmented signal extraction from clinical documentation. Structured data drove the core decision graph, while the language model identified contextual nuance from notes that rules alone would miss. Designed graduated levels of autonomy: fully automated for high-confidence, well-defined actions and human-in-the-loop for ambiguous or high-stakes recommendations.
Reduced average care gap identification time by over 70%. Coordinators shifted from manual chart review to exception-based workflows, focusing their time on complex cases where human judgment mattered most.
Real-time data access from the analytics warehouse was too slow for application queries. The team needed sub-second response times on patient data without compromising HIPAA controls.
Orchestrated incremental sync pipeline with upsert logic, private network connectivity across cloud projects, and a lightweight API proxy for application-layer access.
Sub-second query latency on live clinical data with full HIPAA boundary compliance and no third-party connectors.
Clinical and operational data lived in dozens of disconnected sources: EHRs, billing systems, lab platforms, and manual spreadsheets. Leadership had no unified view of patient populations and couldn't support any analytics or AI initiatives until the data problem was solved.
Built a centralized cloud data platform on GCP with automated ingestion from all major source systems. Established a governed data lake with standardized schemas, access controls scoped to PHI sensitivity levels, and a transformation layer that produced clean, queryable datasets for downstream consumers.
Single source of truth across clinical, financial, and operational data. Reduced reporting turnaround from weeks to hours and created the foundational layer that later supported predictive readmission models.
The company had a working product but no real data infrastructure. Application databases were being queried directly for analytics, slowing down production systems. The team wanted to add AI features but had no reliable pipeline to feed models with clean, current data.
Designed and deployed a cloud data platform with event-driven ingestion, a staging layer for raw data, and a curated analytics warehouse. Built incremental pipelines that kept the warehouse in sync without impacting production databases. Added data quality checks and alerting so the team could trust what they were building on.
Decoupled analytics from production, eliminating performance issues. Gave the data science team a reliable, fresh dataset to train and validate models against, cutting their feature engineering cycle from weeks to days.
A seed-stage digital therapeutics company used Cursor and Claude to build a patient-facing CBT companion app in under three weeks. The demo impressed investors and early clinician partners, but the codebase had no auth beyond a hardcoded API key, patient session data was stored in an unencrypted SQLite file on a single Cloud Run instance, and there were zero tests. Their compliance counsel told them they couldn't onboard a single real patient until the system was HIPAA-ready.
Ran a two-day production-readiness assessment, then executed the full vibe-to-prod playbook. Stood up a data platform first: Cloud SQL (PostgreSQL) with encryption at rest, a BigQuery warehouse for analytics, and event-driven ingestion pipelines so the application never touched the warehouse directly. Then hardened the app layer — added Firebase Auth with RBAC, migrated all PHI into properly scoped database schemas with audit logging, implemented row-level security, and wrote integration tests covering every patient data path. Deployed behind Cloud Armor with WAF rules, set up structured logging to Cloud Logging, and created runbooks for the two-person engineering team.
Went from a demo that couldn't legally touch patient data to a HIPAA-compliant, BAA-covered production system in six weeks. The company onboarded its first clinical pilot cohort on schedule, and the data platform we built became the foundation for their Series A analytics story.
After migrating to GCP, the organization had cloud infrastructure but no data strategy. Teams were spinning up ad-hoc queries and one-off exports. There was no consistent way to measure clinical outcomes, and AI vendor evaluations kept stalling because no one could provide clean training data.
Implemented a structured data platform layer on top of existing GCP infrastructure. Consolidated EHR extracts, claims data, and patient-reported outcomes into a governed warehouse with role-based access. Built reusable transformation pipelines and a catalog so both analysts and future AI workloads could discover and trust available datasets.
Enabled the first organization-wide clinical outcomes dashboard within 6 weeks. More importantly, created an AI-ready data foundation that allowed the team to move forward with an NLP vendor evaluation using real, validated patient data.
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