Every engagement is scoped, priced, and dated up front. We engineer the systems that make AI behave the same way twice — efficiently, repeatably, and measurably — and we leave you with the playbook to keep doing it after we go.
The purpose of a harness is to get consistent quality results — the same right answer, twice, across the kinds of inputs your users actually send. We design and build the loop, in your stack, by engineers who have shipped this five times before.
An end-to-end task-completion rate that moves from around 0.55 to above 0.80, with answer variance cut by more than half — and the eval suite to defend both numbers on Monday morning.
Repeatable quality starts with knowing what quality is. We build the evaluation system your team will use long after we're gone — the harness that turns AI improvements into measurable deltas, and catches the regression you didn't know to look for.
The eval suite catches a regression within the first month. Your team stops shipping AI changes on vibes — and starts shipping them with the same discipline as any other code change.
For teams that already have the engineers. We're the sparring partner who has seen how AI systems fail — and what keeps them reliable — in your Slack, in your PRs, in your weekly review.
Your team makes fewer architectural mistakes that take six weeks to unwind. AI features get shipped with the same repeatable discipline as the rest of your engineering — because the second opinion is already in the room.
Built on our proven AgentWorkforce architecture — in production today — we design a fleet of specialist agents around a triage router, with shared memory and multi-channel reach. You don't get a chatbot; you get an AI workforce.
Architecture deep-dive →A platform of 4–8 specialist agents your team uses daily — via chat, CLI, and scheduled jobs — with new capabilities added by editing YAML, not by waiting on engineering.
You don't wait until the end to see results. Each phase ships something you can use, measure, and trust — long before we're "finished."
You've already paid to build your system. Code Automation isn't a rewrite — it's the maintenance layer that keeps what you've built healthy, secure, and improvable for years to come.
We start by mapping what's already there — architecture, dependencies, technical debt — before we recommend a single change.
The pipeline integrates with your existing workflow. Your current developers keep working normally; the automation runs alongside.
As part of onboarding, we auto-generate documentation for everything that exists today. For many clients, this is the first complete picture they've ever had of their own system.
The initial audit surfaces existing vulnerabilities. We remediate them as part of integration — not as a separate, billable engagement.
The codebase stays yours. The pipeline is built on industry-standard platforms — GitHub Actions, your cloud, your CI — not a proprietary box you can't open.
Rarely well. Our process assumes someone on your side reads PRs and owns the system after we leave. If that's not in place, we'll be honest about it on the intro call.
Yes. Mutual NDA before the brief; full MSA before code touches your repo. We use a standard template, edits welcome.
Python, TypeScript, Go — in that order of frequency. We've shipped harnesses on Anthropic, OpenAI, Bedrock, Vertex, and open-weights models running on vLLM, TGI, and Modal.
Probably not. We typically have a 3–5 week lead time and run two engagements at most. We'll tell you the truth on the call — and if we can't help in your timeline, we'll point you to people who can.
Most engagements add a second phase. We re-scope, re-quote, and re-date — same discipline as the first one. There is no "we'll figure it out as we go."
A two-week eval engagement. Below that we're not adding much you couldn't do yourself in a focused sprint.
A 30-minute call. Bring the problem. We'll tell you what we'd do.