AI Integration
Get your team actually using the AI tools you already pay for
Most teams I see fall into one of three buckets: they bought Copilot licenses and three people use them; they're scared of "letting AI write the code"; or they let AI write everything and now have a codebase that surprises them every Monday.
There's a middle path, and it takes more than a Slack announcement.
Sounds familiar?
- You bought AI tool licenses and adoption is somewhere between "nobody" and "the one keen junior"
- Your seniors don't trust AI, your juniors trust it too much
- You don't have an answer when someone asks "should I use AI for this?"
What you get
- A team that actually uses AI tools, productively, in your real codebase
- Internal guidelines for when to reach for AI and when not to, written in your team's language
- Hands-on practice, not a slide deck someone forgets the next day
How I work
I show up in your codebase, not your conference room. We pick real tickets and work through them together: where AI helps, where it lies, where the human is faster, where the model needs better prompts.
I'm a tech lead myself, so I think about this from the inside: what survives once I'm gone, what becomes habit, what was just hype.
From real projects
For an investment company I built an AI-powered document pipeline: documents are collected automatically from email inboxes and folders, classified and renamed on ingestion, and filed in the document management system, plus weekly reports on what was stored. Filing chaos turned into overview: documents live where people expect them, and unstructured data becomes information you can act on.
How pricing works
No hourly rates, no price list. We start with a conversation about the problem, and what solving it is worth to your business. You then get a fixed-price proposal in three scopes (lean, full, premium), so you can balance scope and budget yourself.
This isn't a "future of work" talk. It's onboarding for tools your team already has.