Questions, answered straight

We build and optimize production AI infrastructure for engineering teams: cutting LLM costs, adding evals and observability, building MCP servers and internal AI-coding workflows, and making codebases ready for agentic development. Every engagement ships working software, not a slide deck.

Two ways. A fixed engagement of $15–40k, or outcome-based pricing where we keep 25% of your documented first-year savings. Outcome pricing means we only win when you measurably save — and it usually bypasses procurement.

The cost optimization engagement is 4–6 weeks and targets a documented 30–60% reduction. A codebase readiness audit is 2–3 weeks. Larger platform builds run 6–10 weeks; migrations run 3–6 months.

You should hire — eventually. We help you ship now and hire later, then transition out. We move faster because this is the only thing we build, and our open-source work (brat, harmony-protocol, fast-litellm) is the same infrastructure we deploy for clients.

Yes. We are model- and vendor-agnostic and integrate with what you already run — OpenAI, Anthropic, open-weight models, your CI/CD, Datadog, Linear, and your internal services. We default to the most capable models but route for cost where it makes sense.

A 30-minute technical call to scope the problem, then a fixed-scope proposal. For cost work we start with a read-only audit of your traffic and prompts; for platform work we start with a short discovery against your repo. No long procurement cycle to get started.

Yes. We work under NDA, prefer read-only access for audits, and for regulated teams we can stand up self-hosted inference so nothing leaves your environment. Audit logs and access control are built into every MCP and tooling integration we ship.