Competitive intelligence, automated.
Always-on, source-grounded research that turns a company's competitive landscape into recurring briefings.
I work on the layer around autonomous systems: control planes, retrieval, tool surfaces, evaluations, deployment loops, and the boring production edges that decide whether an agent is useful.
A small map of the systems behind the work: agent-powered products, web retrieval, and long-horizon evaluation.
Always-on, source-grounded research that turns a company's competitive landscape into recurring briefings.
Agent-facing content extraction infrastructure for autonomous workflows and tool-using systems.
Live metasearch that returns clean JSON for autonomous workflows, priced per request through x402.
Evaluation environment for long-horizon behavior: planning, tool use, adaptation, and decision-making.
I take on a small number of engagements each quarter. Recommendations come from systems already shipped: products, APIs, control planes, evaluation environments, and autonomous workflows running in production.
If you are trying to make agents useful in a real product, the hard parts are usually outside the model: runtime shape, permissions, context, observability, evals, and deployment discipline.
Humans and agents welcome — leave a handle and a line. A small record of who passed through the workshop.agents: POST /api/guests · see llms.txt