Every engagement runs on the same principle: AI does the heavy lifting, a human stays in control. Start small with a readiness review, or go straight to standing up the full workflow on your system.
A fixed-scope look at your codebase and how your team actually works. I tell you honestly where AI-assisted development fits, where it doesn't, and hand you a short, practical roadmap — no commitment beyond the review. It's the low-risk way to find out if this is for you.
Mix and match, or take them in sequence. Most people start with a review or a single pilot feature, then scale up once they've seen it work on their own system.
The guardrailed pipeline installed and tuned on your codebase — plain-English requests in, reviewed pull requests out. Includes the custom skills, slash commands, permission hooks, and the human-owns-the-merge review gate, wired to your stack and your tests.
Scripted, production-like copies of your existing system — one per person, each fully isolated with its own database and its own data. Everyone gets their own sandbox to experiment, build, and test in — without stepping on each other, and without ever touching production. Cheap to run, and rebuildable from scratch on demand.
Your safety net, written in plain language. Describe how a feature should behave and the AI writes the end-to-end tests — built on Playwright and driven by Gherkin / Cucumber BDD, so every test reads as a plain scenario (Given… When… Then…) a non-developer can read, approve, and own. Executable specifications that double as living documentation, run on every change across browsers — catching regressions before your users do.
When an idea outgrows the core system — a customer portal, an internal tool, a mobile companion — the workflow launches a brand-new project from a hardened template: modern stack, CI/CD from day one, and a clean API contract back to your existing data (never direct database access). Non-developers can spin up and grow these connected satellite projects the same plain-English way. Monolith, a fleet of (micro)services, or a mix — each piece gets its own guardrailed pipeline and sandbox, under one consistent review gate.
Teach the people who'll actually use it — product managers, ops, owners — to drive the workflow in plain English, and equip whoever owns the review gate to sign off with confidence.
Agents, internal automations, API and data integrations — scoped to one concrete outcome, not an open-ended experiment. The broader "I have an AI problem" bucket.
There's no big-bang commitment. Most people step through this ladder — each rung earns the next, and you can stop wherever it makes sense.
Engagements run as fixed-scope projects, an ongoing partnership, or a mix — whatever fits how you work.
That's exactly what the Readiness Review is for. Tell me about the system you'd love to move faster on, and I'll point you to the right starting rung — or tell you honestly if it's not a fit.