About
A small applied AI lab focused on useful systems.
Vorp Labs studies the gap between frontier model capability and operational impact. The work is intentionally practical: workflow evals, AI coding cost audits, enterprise data agents, inference cost engineering, retrieval systems, and the product patterns that make AI dependable.
The public site is a place to publish notes, benchmark specs, small tools, and calls for real-world workflow examples.
Principles
Systems over demos
A model response is not a product. Useful AI requires context, tools, evals, interfaces, and failure handling.
Specific beats general
Narrow workflows make it possible to measure quality, reduce cost, and decide when small models or deterministic code are enough.
Evidence over posture
We prefer task definitions, latency numbers, failure modes, and caveats to broad claims about intelligence.