About
I'm an AI engineer focused on building trust in production AI systems.
Most AI deployments I've seen have the same gap: The system produces fantastic results most of the time, but this only makes the consequences higher when it is confidently wrong.
I build the infrastructure that closes that gap: evaluation pipelines, accuracy monitoring, and alerting systems for AI deployed on high-stakes documents and workflows.
The clients I work with use AI for contract review, financial document processing, customer communications, and revenue operations. The common thread: high cost of error, no visibility into quality or pre-emptive failure control.
I also wrote a practical guide to language models — a technical reference for engineers building with LLMs rather than just using them. Read it →