For VP Engineering
Your team is changing faster than your infrastructure
Last verified: June 2026· VP of Engineering engagement
AI coding tools are reshaping how your engineers work, your AI features have no evals, and your LLM bill keeps climbing. We help you get ahead of all three — with working software, not strategy decks.
You don’t need another consultant who can’t pass a technical screen. You need an engineer who has shipped production agent infrastructure and can tell you, straight, where your time and money are going — and fix it in weeks.
What’s on your desk
AI coding tools are a free-for-all
Claude Code, Cursor, and Copilot are in your org but there’s no golden path. Juniors are 2x faster; seniors say quality is slipping. The gains aren’t compounding because nothing is standardized.
Your AI features have no evals
You shipped them a year ago. Now every prompt change is a blind deploy and the cost of a bad output only shows up after it reaches a customer.
The LLM bill keeps climbing
Spend is one line item on a cloud invoice. Nobody can say which routes or models are driving it, so every proposed cut is a guess.
The codebase isn’t agent-ready
You suspect it but can’t quantify it. Thin tests, fragmented context, no specs — agents thrash where they should fly.
How we’d help
The engagements that fit a VP of Engineering best — each ships working software and a measurable result.
LLM Cost & Performance Optimization
Production LLM spend usually grows faster than anyone’s ability to see inside it. We baseline it per route, audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
A measured cost reduction against a recorded baseline, quality held
Production AI Eval Infrastructure
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.
An eval platform wired into your CI/CD
Internal AI Coding Workflow Build-Out
We standardize how your team uses Claude Code / Cursor / Copilot — custom slash commands, agent definitions, MCP servers for your internal tools, golden-path templates, and code-review hooks.
A working internal AI dev platform your team adopts
Agentic Codebase Readiness Audit
We map your codebase against what actually makes it AI-coding-ready — module boundaries, test coverage, type strictness, docs, CLAUDE.md / rules files, and MCP potential — and quantify how far off you are.
A scored report + prioritized remediation roadmap
The proof is open source
We deploy the same infrastructure we build in the open.
Pick the fastest win and start there
Most VPs start with the LLM cost audit — it pays for itself fastest and tells us both whether there’s a bigger build worth doing.
The internal conversation you have to win
Most VPs we work with do not have a technology problem so much as a sequencing problem. Four things are all somewhat broken at once — the coding-tool rollout, the missing evals, the climbing bill, the codebase nobody wants to hand an agent — and each has an advocate arguing it should go first. The order that actually works is dictated by what makes the next step measurable: instrumentation before cost work, because you cannot right-size a route you cannot isolate; evals before any model change, because a downgrade without a quality signal is a bet; readiness work before a platform build, because a harness on top of an illegible repository amplifies the mess.
The second conversation is about who owns the result. Engagements that leave nothing behind are common in this space, and the tell is a deliverable that is a document. We would rather pair with an engineer on your side from week one, even at half their time, than hand over a system cold at the end. They learn the thing as it is built, they are the person who says what fits your organisation, and they are the reason it still works six months after we leave. Where that person does not exist, helping you define the role is legitimately part of the work.
The third is expectation management upward. Your executive team has probably heard a number about AI productivity and would like you to confirm it. The defensible position is that generation gets faster, review does not automatically follow, and the honest measure is delivery rather than output — which means change lead time and change failure rate, reported together, over a quarter rather than a fortnight. Arriving with that framing before someone else arrives with a vendor's acceptance-rate chart is worth more than any individual technical decision on this page.