Service 01
AI Strategy & Advisory
Know what to build, what to skip, and what will freeze in production before you write a line of code.
- Scoping
- Feasibility
- Written report
Most of these projects fail in the parts nobody demos.
A prototype that works on twenty examples tells you very little about the thing that has to work on twenty thousand, at three in the morning, on data that arrived in the wrong shape. The decisive questions are the dull ones: where does the input come from, what happens when the answer is wrong, who finds out, and what does one request cost when it runs a million times a month.
We answer those before anyone commits a budget. Sometimes the answer is a smaller system with no model in it at all — and we would rather you heard that from us now than from your own logs in six months.
What we actually do
- Work through the problem with the people who do the job today
- A straight read on what current models can and cannot do here
- Where the data would have to come from, and whether it exists in a usable state
- Cost per request and per month, at the volume you actually expect
- The failure modes written down, with what each one would cost you
- A written recommendation — including “don’t build this” where that is the answer
Who it’s for
Teams about to commit real budget, and teams holding a prototype that impressed everyone in the room and now has to survive customers.
How it runs
One to three weeks, ending in a written report and a call to go through it. No obligation to build it with us afterwards.
Start with the problem.
Tell us what you’re working on and whether ai strategy & advisory is really what it needs. An engineer reads every enquiry.