[Draft note for Manuel]: same note as the ES version — this describes the service you offer as a consultant, distinct from the personal case-study project file. Flag if you'd rather merge or drop one to avoid duplicating the message.

One of the clearest shifts in how software gets built today is the disciplined adoption of AI tools into the development cycle — not as a shortcut, but as a way to move faster without losing rigor. I help engineering teams make that transition well.

In my own recent experience on enterprise projects (e-commerce platforms and financial services, with integrations to systems like Salesforce, SAP, and legacy systems), I brought Claude, Cursor, and GitHub Copilot into a spec-driven development workflow: before writing code, expected behavior, system constraints, and edge cases get defined clearly — AI assists with implementation within those explicit boundaries, not from a vague idea.

What's typically included:

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

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

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