AI developer onboarding

What we do

• Tooling, set up properly. Copilot, Claude Code, and agentic IDEs configured against your repo, your conventions, your CI — not a generic install. Project memory and rules so the agents read your codebase the way your team reads it.
• Prompt & context engineering. Reusable prompt patterns, codebase rules, agent skills, and the small habits that separate a team shipping with AI from a team fighting it.
• Agentic workflows on your stack. MCP servers wrapping your internal APIs and dev tools, agents that can safely read tickets, run tests, open PRs, and close loops inside your systems.
• Evals & guardrails. Lightweight eval harnesses for prompts and agent flows, regression checks in CI, and the decision-record discipline that keeps AI changes auditable.
• Code review in the AI era. What changes when half the diff was drafted by an agent — review checklists, trust calibration, the failure modes we've already burned a finger on.
• Team enablement, not just tooling. Pairing sessions, internal playbooks tuned to your stack, ongoing office hours. Your engineers own the workflow once we step aside.

How an engagement runs

1. 01 Diagnostic — half-day with engineering leadership and a couple of senior devs to understand your stack, your pain points, and what "good" looks like for your team.
2. 02 Workshop — two days on-site, your engineers, your repo. Tooling installed, rules written, two real tickets shipped end-to-end with the AI workflow.
3. 03 Embed — 4 to 8 weeks of part-time pairing with the team while the workflow becomes routine. Weekly office hours, async review, playbook iteration.
4. 04 Handover — a written playbook customised to your stack, eval harness in CI, and the team owns it from there. We're a phone call away, not a dependency.