Helping engineering teams use AI well.
A group of senior software engineers watched AI split tech teams into four groups:
- The Experts: using AI tools well and getting real value from them.
- The Reckless: pasting proprietary code, personal data, and API keys into prompts without thinking.
- The Holdouts: refusing to touch AI because they're scared for their jobs, and falling behind.
- The Inefficient: using AI safely but badly, burning time and tokens without knowing how to manage context or delegate work.
We started Burghley AI Services so nobody gets left behind as AI keeps changing how software teams work.
We figured out how to fix these problems by fixing them on our own teams first. We analyse the sanitised findings from your codebase scan to map how your team uses AI day to day, then turn what we find into four detailed reports: a technical audit, a management summary, a best practices plan, and per-developer training guidance. It is not AI governance in the legal sense and it is not an audit of the models themselves. It is an audit of how your people actually use them.
Where this audit came from
It started with one team. Some engineers were careful about what they shared with AI tools. Some threw everything at them, no questions asked. Some refused to use them at all. Some used them safely but badly, wasting time and money they didn't need to. We fixed that split and turned it into shared, sensible practice. It's the same work we now do for other software houses.
THE FOUR PATTERNS WE SAW
Responsible users, reckless overusers, AI-avoidant holdouts, and well-intentioned but inefficient users. Every engineering team we've audited since has a mix of all four. We find out which, and where.
Core Principles
Specificity
We don't deal in generalities. Every finding is tied to a real pull request, a real file, or a real pattern we saw in your codebase - not generic advice about "using AI responsibly."
Practicality
The output is a code of conduct your engineers will actually follow, not compliance theater. If a rule doesn't survive contact with a real sprint, we don't write it.
Staying Current
AI tools and team habits keep moving. Book another one-off scan any time your stack, your tools, or your team's usage change: same flat fee, compared against your original baseline to track what shifted.