Production AI Eval Infrastructure
Last verified: June 2026· engagement
Most teams shipped AI features with zero evals. We build eval harnesses, regression suites, online quality monitoring, and A/B infra for prompts and models.
- Outcome
- An eval platform wired into your CI/CD
- Timeline
- 4–8 weeks
- Pricing
- $30–80k build + $3–8k/mo ops
- Buyer
- VP Eng, Head of ML / AI Platform
The problem
You shipped AI features with zero evals. Every prompt or model change is a blind deploy, and the cost of a bad output only shows up after it reaches a customer.
What we do
- Build eval harnesses and regression suites for your prompts and models.
- Add online quality monitoring and alerting for production traffic.
- Stand up A/B infrastructure for prompts and model swaps.
- Wire it all into your CI/CD so quality is a gate, not a guess.
How it fits together
What you get
Built on our open source
openclawOS — An OS-like architecture for AI assistants — a kernel-based design with process-isolated apps.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
Guides for this work
How to build LLM evals
Evals turn 'the model feels better' into a number you can gate a deploy on — they are the test suite that makes every prompt, model, and pipeline change safe to ship.
LLM evalsWhat is RAG (retrieval-augmented generation)?
RAG retrieves the passages relevant to a question and puts them in the prompt, so the model answers from your data with citations instead of relying on what it happened to memorise during training.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.