For VP Engineering
Your team is changing faster than your infrastructure
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
→Most teams running production AI pay 3–10x what they should. We audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
30–60% LLM cost reduction in 4 weeks, documented
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.