I package pipelines so they’re easy to deploy, scale, and maintain — no duct-taped scripts, no fragile environments.

I build pipelines with security and compliance baked in — not bolted on after the fact.

I measure everything — retrieval, intent classification, and latency — because without benchmarks, you’re just guessing.

I design semantic search systems backed by vector databases that return results which are fast, relevant, and explainable.

I design embedding-driven intent classifiers that map natural language into safe, structured actions for automation.

I design retrieval-augmented generation pipelines that ground model responses in real data, delivering answers that are verifiable, accurate, and domain-specific.

I design embedding pipelines and vector stores that balance accuracy, speed, and scalability for domain-specific retrieval.

I design tools that parse, chunk, and organize data into structured knowledge stores built specifically for retrieval-augmented generation.

I don’t rely on brittle, one-off scripts — I engineer modular scraping frameworks that reliably extracts, cleans, and structures technical content at scale.

I don’t rely on brittle, one-off scripts — I design modular scraping frameworks that reliably extract, clean, and structure technical content at scale.