Most AI tools work fine in demos. They break in production.

Outputs that looked great in testing start hallucinating. The workflow that worked on 10 rows fails on 500. Nobody knows why.

I dig into that.

I audit your existing AI pipeline- whether it's an agent, a workflow, or an LLM integration find exactly where it's failing, and fix it. Not with guesswork. With a proper eval harness built against your actual data and edge cases.

What I typically audit:

Agents that give inconsistent or wrong outputs

LLM workflows with no reliability checks

Automation pipelines that fail on real-world data

Prompts that work sometimes but not always

What you get:

A written audit of every failure point

An eval harness tested against your real data

Fixes with documented reasoning

Confidence that it actually works before you ship it

If you’ve built something with AI but wouldn’t bet your business on it yet, that’s exactly what this is for.