AI systems / retrieval / evals / architecture

AI systems that hold up in production.

I build agent workflows, retrieval layers, and internal AI platforms that stay legible under real constraints through grounding, evals, observability, and disciplined integration work.

Best fit: teams where the model is no longer the interesting part.

What I audit first

The first pass is usually enough to tell whether the system is trustworthy, merely busy, or headed for a political meeting.

  1. 01

    Retrieval quality and fallback behavior

  2. 02

    Tool boundaries and approval paths

  3. 03

    Evaluation coverage and failure criteria

  4. 04

    Telemetry, rollback, and operator visibility

  5. 05

    Ownership once the system is live

Writing

Notes on systems, delivery, and the failure modes in between.

Notes on grounded AI systems, evals, observability, and integration-heavy delivery once the demo is no longer the hard part.

Assistant

A bounded assistant for public questions.

Ask about grounding, architecture, delivery style, or role fit. It uses the public profile, published writing, and site metadata.

Architecture, delivery, retrieval, fit

Ready

Ask about system design, delivery, or fit.

A few useful starting points are built into the chat.

Contact

Available for selected architecture roles, focused audits, and embedded build shaping.

Best for teams with a live system, a trust problem, or an integration mess that has started to become political.