AI in Production
AI that ships, and keeps working.
An engagement that takes AI from an idea to a system your team uses every morning. We prove it on your own data before there is a budget for a build, then we build the loop around the model: the input, the exception, the integration and the monitoring. If the prototype does not hold up, we say so, and you have spent weeks instead of a year.
A model is one box.
Production is the other five.
Most AI work stops at the box in the middle, because that is the part that demonstrates well. The value, and the risk, live in everything around it.
Input
Documents, cases and records, in any format, in any language.
Model
Extraction, classification or risk scoring.
Confidence gate
How sure is sure enough, decided field by field.
A person decides
Ranked queue, one screen, an audit trail behind every decision. Then it rejoins the path.
Integration
Posted into the system that acts on it.
Monitoring
Accuracy measured after launch, drift caught early.
The fork is the design. Automation with no exception path is not a saving, it is a liability moved somewhere nobody is watching.
Four phases, and one point where you can walk away.
Frame
One workshop with the people who do the work today. We map the process, the volumes, the cost of a single error and the systems the result has to reach. Sometimes the honest conclusion is that a rule and a form solve it, and we say that.
Prove
The model runs against your real material, including the cases that break rule based tools. You see accuracy per field and per document type, and the failures as clearly as the successes. Nothing is generalised from a clean sample.
The line stops here on purpose. Walking away at this point is a normal outcome. You keep the report, the scope and the numbers, and nothing further is committed.
Build
The model, the confidence gate, the review screen, the audit trail and the integration into the system that acts on the result. Working software every two weeks, tested before production and not by it.
Run
Accuracy tracked in production, drift caught, the model updated when the inputs change. Documentation, training and a support agreement, so the process survives people changing jobs.
For Duo Cosmetics, vendor invoices in any layout and in two languages are read, checked against price history and product codes, and posted into Odoo as draft bills. Routine invoices pass without a person. Anything unexpected stops and waits for one.
One team, seventeen capabilities
AI & Automation is the capability behind this service. The same team carries the rest, which is why the model ends up inside a real system instead of beside one.
Highlighted is the capability behind this service. The rest is on the same team.