For Developer Experience
Turn the AI coding free-for-all into a golden path
Last verified: June 2026· Head of DevEx engagement
Adoption is happening with or without you. We help you standardize it — so juniors and seniors both get faster, and quality goes up instead of down.
The productivity is real but it’s not compounding, because everyone uses the tools differently. We build the internal AI dev platform that makes the right workflow the default one.
What’s on your desk
Quality is slipping as speed rises
Juniors ship faster with agents; seniors catch more issues in review. Without standards and guardrails, velocity comes at the cost of the codebase.
Every engineer reinvents the workflow
No shared slash commands, agent definitions, or templates. The team’s collective learning evaporates instead of accumulating.
Internal tools aren’t agent-accessible
Your devs can’t point agents at company infra safely, so a whole class of high-value tasks stays manual.
No measurable adoption story
Leadership asks if the AI tooling investment is paying off and you don’t have the instrumentation to answer.
How we’d help
The engagements that fit a Head of DevEx best — each ships working software and a measurable result.
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
MCP Server Builds for Internal Tooling
We expose your internal systems — Datadog, Linear, databases, deploy tooling — to AI agents over MCP, so your devs can work against company infra safely.
Internal MCP servers with auth, access control, audit logs
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
Agentic Development Platform Build
A brat-style multi-agent harness tuned to your stack. Your devs delegate tasks — refactor a module, add tests, fix a class of bugs across the codebase — with safety rails and human-in-the-loop.
A multi-agent harness running against your repo
The proof is open source
We deploy the same infrastructure we build in the open.
Ship the golden path your team adopts
We’ll build the slash commands, agent definitions, MCP servers, and review hooks — and roll them out until adoption sticks.
Why AI-coding rollouts stall, and what unsticks them
The most common failure is not resistance but fragmentation. Two or three engineers quietly build something excellent for themselves — a set of prompts, a way of framing tasks, a small script that assembles the right context — and nobody else ever sees it. Meanwhile the rest of the team is told the tool is available and left to discover the same things independently, badly. Finding those private setups and generalising them is usually the highest-return week of a DevEx engagement, and it costs nothing but attention.
The second is that a golden path imposed as policy does not survive. Engineers adopt a workflow because it is faster for them this week, not because a memo said so, and a mandated path they find slower gets routed around within a sprint. The version that sticks is opt-in, obviously better at the specific task it targets, and narrow enough to be maintained. Five task templates people actually use beat forty that were generated once and never revised.
The third is the review bottleneck, which is the failure mode that masquerades as success. Generation gets cheaper, more code arrives at review, review capacity does not change, and lead time stays flat or worsens while every adoption metric climbs. If you measure only adoption you will not see it. Watching review hours per reviewer per week alongside the rollout is the cheapest early warning available, and when it moves the right response is investment in what makes review cheap — faster tests, smaller changes, clearer diffs — rather than more seats.
There is also a question about juniors that deserves a straight answer rather than a slogan. Coding agents genuinely accelerate people who can evaluate the output and genuinely mask gaps in people who cannot, and the second effect is invisible for months. The teams that handle this well tend to be explicit about it: agents are for work the engineer could have done, review is where learning happens, and nobody merges something they cannot explain. That is a cultural norm rather than a tooling decision, and it is worth setting deliberately before the habit forms on its own.