What kinds of teams need this most?
Teams already shipping AI-enabled features or internal tools usually feel this pain first, especially when prompt or model changes start causing regressions.
AI Evals and CI Services
Add eval gates, CI checks, and observability so AI-enabled systems ship safely under change.
Who this is for
- Teams shipping AI features without robust release checks
- Engineering orgs needing confidence in prompt/model updates
- Builders who want measurable quality signals before deploy
What I help with
- Turn manual AI spot-checks into repeatable evaluation and release gates
- Track quality, latency, and cost drift before changes hit production
- Build confidence around prompt, model, and toolchain updates
How I work
- Discovery: define the user-critical scenarios that should gate release decisions
- Workflow design: choose eval sets, failure thresholds, and where checks should run
- Implementation: wire evaluation logic into CI, release paths, or review tooling
- Validation and observability: track quality, latency, and cost signals over time
- Handoff and documentation: leave a clear operating model for evolving the checks
Proof / related work
Relevant project
- OpenClaw Local Operator System | OpenClaw is a local-first operator system built around Discord control, a visible work ledger, Mission Control state, and bounded execution lanes instead of vague agent autonomy.
- YT Content Factory | YT Content Factory is a local-first AI video production system built around lane isolation, explicit QC, fallback behavior, and honest release gates across short-form and longform output.
Related writing
- My Approach to LLM Infra, Evals, and CI | Prompt tweaks are not strategy; eval gates are.
- What Quality Gates Actually Matter in AI Video Pipelines | Passing a render is not the same as clearing a release gate.
Typical engagement options
- Eval strategy and release criteria definition
- CI integration for automated AI quality checks
- Observability and drift-monitoring setup
- Review pass on an existing AI release workflow
Frequently asked questions
Do you only work with greenfield AI stacks?
No. Most of this work is about improving an existing release process, evaluation set, or observability setup without stopping delivery.
How do you handle reliability and observability?
I focus on release-relevant signals: quality checks, failure thresholds, latency, and cost, then make sure they are visible where deployment decisions happen.
Can this work with Python, n8n, or LLM-based stacks?
Yes. The underlying tools can vary, but the evaluation and release discipline still applies across Python services, workflow orchestration, and LLM-powered systems.
Is this only about CI, or also about ongoing ops?
It covers both. CI catches regressions early, but observability after release is what tells you whether the system stays healthy under real usage.
Need help with this?
If you need help building reliable automation or internal AI systems, let's talk.
Why work with me
- I bias toward practical systems, not automation theater or vague AI promises.
- I treat reliability, observability, evals, and operator handoff as part of the scope.
- I leave behind documentation and decision context so your team can keep shipping after handoff.