AI Systems & Product Engineer
who ships AI at scale
with Evals + LLMOps + HITL

AI agent evaluation platform that certifies whether LLM agents are reliable enough for unsupervised operation, benchmarking GPT-4o, GPT-4o mini, Claude Sonnet 4.5, and Gemini 2.5 Flash across simulated business workflows.

• •Live evaluation pipeline making real per-trial LLM API calls with randomized edge-case injection, tracking per-trial latency and cost for cost-vs-reliability tradeoff analysis.
• •Scoring system separating benchmark accuracy from real-world reliability, surfacing gaps as large as 61 points between the two.
• •Automatic Cleared / Supervised / Not-Ready classification with configurable thresholds, tracking unsafe or irreversible actions as a distinct failure category.
• •Full reporting layer with dashboards, leaderboards, and expected-vs-actual failure breakdowns to make results auditable.

Self-directed architecture proposal extending Everpure's Data Intelligence platform with a portable data-governance layer, gating AI and RAG access by sensitivity, legal basis, and consent. Modeled across two real regulatory regimes using Everpure's own public customer case studies.

• •Identified a real gap in Everpure's published healthcare and banking case studies: no automated way to separate regulated data from data safe for AI use.
• •Designed the Trust Passport, a portable metadata record (sensitivity score, legal basis, retention, AI-eligibility, protection tier, carbon budget) that five existing Everpure systems can act on automatically.
• •Proved the same architecture holds under both a US sectoral law (HIPAA) and an EU rights-based law (GDPR/DORA), evidence the pattern generalizes to any regulated industry.
• •Benchmarked against named DSPM competitors (BigID, Varonis, Cyera, Securiti) to show none pair classification with the physical storage layer the way this design does.