The problem
In many businesses, part of the work repeats every week: sorting mail, classifying requests, re-keying, deciding the simple cases. That work costs time to the people who should be handling the cases that matter. An AI demo solves nothing; what solves the problem is a solution that runs in production, that your teams stay in charge of, and whose result is measured.
How I put AI in place, with your teams
The principle is always the same: start from the case that costs time, keep your teams in charge of the solution, and measure in production.
Frame the case
Together I pick one precise process: inbound mail, a category of requests, a data entry. I write down what will be automated, what stays human, and how the result will be measured.
A model that learns, a person who decides
A model trained on your teams’ past decisions; a confidence threshold below which the case goes up to a person; their corrections retrain the model, which keeps learning in production.
Build with your teams
The solution is built with the people who will use it. Managed for what is not differentiating, bespoke for the core, with the running cost announced up front.
Go live and measure
Staged go-live, separate environments, deny-by-default authorization. The result is measured; I extend only if it holds over time.
A measured example
For a tour operator, an operations platform handles inbound mail with this model, on an event-driven, serverless architecture. Mail arrives attached to the right file, uncertain cases go up to an operator, and every correction trains the model.
Your data stays under your control
The architecture is chosen for that: separate environments, deny-by-default authorization, hosting that fits your constraints. Your teams see what is automated and can correct it.
Frequently asked questions
Where do I start?
With one concrete case that costs time every week. I frame it together, measure, and extend if it holds.
Will my teams lose control?
No. The model learns from your teams’ decisions, a person validates the uncertain cases, and every correction retrains it. Decisions are written down.
What if the model is wrong?
Under a confidence threshold, the case goes to a person. Their correction retrains the model. The acceptable error rate is set by you, not by the algorithm.
Do I need a big project to start?
No. I start with one process, in production, measured. Extension comes after.
A concrete case on your side?
Tell me what costs you time; I get back to you within 24 business hours with a first read, no obligation.