Case Studies

A high-growth FinTech startup was burning $60,000/month on OpenAI API bills. By implementing a semantic caching layer and "Model Routing" architecture, I reduced their monthly bill to $4,800 (92% reduction) while improving average response times by 400ms.

Architected an ML pipeline processing 30fps video streams from 50+ cameras simultaneously, achieving 97% threat detection accuracy and preventing $2M in annual losses.

Built a production semantic search system handling Spanish and English queries across 1M+ documents with sub-100ms p99 latency using embeddings and vector databases.

I built GrindProof as my "architecture laboratory" where I test AI accountability mechanisms, behavioral patterns, and scalability approaches with my own resources before implementing them in client production environments.

I was dropped into a five-person engineering team that had never shipped an AI feature before. We rebuilt their delivery process, set up evaluation harnesses, and paired on architecture until they shipped confidently without me.

Client wanted an AI recommendation engine. After analysis, I discovered their "ML problem" was actually a data quality issue. A well-designed Postgres query solved it 10x faster and cheaper.