One of the clearest shifts in how software gets built today is bringing AI tools into the development cycle with discipline. Not as a shortcut, but as a way to move faster without losing rigor. We help engineering teams make that transition well.

We've done it on enterprise projects — e-commerce platforms and financial services, with integrations to Salesforce, SAP and legacy systems — bringing Claude, Cursor and GitHub Copilot into a spec-driven development workflow: before any code gets written, expected behavior, system constraints and edge cases are defined. AI implements within those explicit boundaries, not from a vague idea.

What's typically included:

Diagnosis of where your team loses time today — exploration, boilerplate, code review — and where AI actually helps.
A spec-driven workflow adapted to your stack, not a generic recipe.
Hands-on support through the first weeks of adoption, focused on keeping real tests, architecture review and code quality intact. AI accelerates; discipline stays human.

It matters most in systems that can't afford to break anything: high-traffic platforms, CRM and ERP integrations, event-driven architectures.

What we don't do: promise a productivity percentage before seeing how your team works. That number depends on the code they already have.

Is your team already using AI in an unstructured way, and you want to bring real discipline to it? Let's talk.