“Building Enterprise ( & Lately AI) systems that scale”
I build AI systems that work in the real world. 17+ years in healthcare, insurance, and fintech means I've shipped under HIPAA, GDPR, and SOC 2 and learned which shortcuts you can't take.
Currently focused on GenAI: RAG pipelines, agentic document workflows, and LLM integration for regulated industries.
Persona-driven GenAI assistant for insurance workflows: claims triage, NDA review, document comparison, and broker submission processing. Each persona controls its own prompts, retrieval scope, and citation rules so users get role-appropriate answers without guardrail workarounds.
Full-stack build of a private markets trading platform: external trading portals, internal ops tools, APIs, caching, and CI/CD with security scanning at every stage. No team, no product, and no infrastructure existed when I joined.
Plaid bank-feed integration that eliminated manual journal entry and month-end reconciliation. Transactions flow into the ledger automatically with categorization and exception routing. Accounting staff went from full reconciliation to exception-only review.
A repeatable path that gets AI past the demo and into something a regulated business can actually run.
Define the eval before the prompt. Pin success metrics, risks and the data that actually matters.
Retrieval, citations and policy baked in. Latency and cost budgeted up front, not bolted on.
DevSecOps, observability and SLAs so it stays compliant and up long after launch.
The gap between a working demo and a production-grade system is where 80% of enterprise AI projects die. Here's what I've learned building both.
CQC, FDA, CE marking — regulatory frameworks for clinical AI are complex and often misunderstood. A practitioner's guide to what actually matters.
After three production agentic deployments, here's the architecture I'd start with — and the mistakes I'd avoid from day one.
He turned a stalled AI pilot into a shipped product in a single quarter.
Open to consulting engagements, advisory roles, and senior full-time opportunities — particularly in AI and regulated industries.
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