Ways to start
AI readiness review
Most failed AI projects did not fail at the model. They failed because the data the model needed was not reachable, nobody could tell whether the output was right, and there was no record of what it did.
Those are answerable questions, and they are much cheaper to answer before a build than during one.
The three questions
What you get
The deliverable
- A go or no-go on each task you are considering — not a general readiness score, which is meaningless.
- The specific gaps that would need closing first, with what each would take.
- An honest cost estimate including the running cost, which is the number most proposals leave out.
- What to do instead, where a rules engine or a fixed script would be cheaper and more predictable than a model.
Why we would rather tell you no
An automation built on data that is not ready does not fail immediately. It works in the demo, works for a fortnight, and then quietly starts being wrong in ways nobody catches for a month. That outcome is worse for you than not building it, and worse for us than losing the sale.
If the answer is not yet, you get the list of what would change that. Some of it you can probably do yourself.
How it fits
The review is a fixed fee and takes days rather than weeks. If it comes back as a go and you proceed to a build, it feeds directly into the scope — the evaluation set produced here becomes the one used to watch the automation in production.
Thinking about building something with AI? Tell us the task you have in mind. If the honest answer is that your data is not ready, you will get that — along with what would need to change.
Get your free review