tracks
● tracks · find the one that fits your team
Let’s start with what’s slowing your team down.
If you’ve brought AI tooling into your studio and the reliability hasn’t caught up yet, you’re in good company — it’s the most common place teams land, and it’s a solvable one. These are the specific problems I help studios work through, each built on tooling I run in my own studio every day. Find the one that sounds like your week, and we’ll scope the depth together — anywhere from a one-week audit to a full build — on a call.
/ start here · not sure which fits?
That’s a great place to start. If you’ve rolled out AI coding tools and the gains haven’t shown up in the data yet — or review has quietly become the bottleneck — an AI Adoption Audit is the easiest first step. In one week you’ll walk away with a clear, prioritized map of where to strengthen your validation infrastructure, and which track below will move the needle first.
Book an AI Adoption Audit →validation
● Track 01 · Trustworthy Agent Output
Trust agent-authored PRs without slowing review to a crawl.
The pain
The agents produce volume, but the PRs ignore your house standards, re-derive context they should already know, and arrive in a shape that makes review the new bottleneck. You’re either rubber-stamping work you haven’t really verified, or you’ve become the human gate that erases the speed-up.
What you get
Two halves of the same problem, scoped together. First, your agents made codebase-aware — a local indexing service that scans your multi-repo codebase and exposes it so an agent queries your patterns instead of re-reading them, and a context rollout that makes agents enforce your standards by default. Second, the validation layer — AI-focused automated testing, CI/CD, and validation agents on your commits, so what reaches a human is already known-good.
telemetry
● Track 02 · AI Telemetry & Governance
Know what your AI spend actually buys — per commit, vendor-neutral.
The pain
Finance sees a token bill, engineering sees activity, and nobody can draw a straight line between the two. When leadership asks what the AI is worth, the honest answer is a shrug. And if you have platform-disclosure or EU-AI-Act exposure on the horizon, “we don’t really track that” is about to stop being acceptable.
What you get
A capture layer that intercepts every model call across every vendor into one analytics database, and dashboards that attribute AI token spend per commit and per release — drillable from a top-line number all the way down to a single prompt. Vendor-agnostic by design, so the record doesn’t depend on any provider’s reporting.
orchestration
● Track 03 · Agent Orchestration
Point a fleet of agents at one backlog without the chaos.
The pain
You’ve got more than one agent working now, and they collide. Two agents grab the same task, dependencies get worked out of order, and a human ends up babysitting the queue — which is exactly the leverage you were trying to buy back. The coordination problem scales faster than the agents do.
What you get
Your team running on coordination infrastructure built for exactly this — atomic task claiming so two agents never collide, dependency-aware ordering, workflow automation, and real-time event streams, installed against your backlog and your workflows. Delivered as a working rollout, not a recommendation.
next steps
Found your problem?
One 30-minute call. We pin down which track and how deep, and if budget needs shaping before a SoW, I’ll help you build the case. Engagements contract through Wood Fired Games.