Automatic document reading
Supplier invoices and receipts that arrive as PDFs become records, with a screen to review what the reading understood before accepting it.
Agents that run the repetitive work, automatic document reading, predictive models on your own sales, and connections between the systems you already have. Inside your processes, not in a demo.
What it solves
The conversation about artificial intelligence usually stops at the pilot that impresses and never goes live. The repetitive work that really costs money — keying in supplier invoices, reconciling two systems, building the same report every Monday — is still done by a person.
What is included
Supplier invoices and receipts that arrive as PDFs become records, with a screen to review what the reading understood before accepting it.
Connected to your systems with limited permissions, they do the whole task: they read, decide by your rules and leave the result recorded.
Demand forecasting, customer churn risk, anomaly detection. With your history, not with an example from another industry.
What moves from one program to another today because someone copies it, moves on its own. It's the dullest saving and the one that gives back the most hours.
Before building, we count how many hours that task costs today. If the number doesn't add up, we say so.
Anything with consequences — a payment, a price, a commitment to a customer — goes through an approval. Automation is not the absence of control.
How it works
How many hours go into that task, and whose.
On your real systems, not on a mock-up.
If it didn't save what was expected, it gets adjusted or withdrawn.
In detail
A misread document that goes in unchecked is worse than typing it: nobody ever checks it again. That's why the review step isn't optional.
A predictor built on three months of data gives a number that looks certain. When there's no basis for it, we say so instead of delivering noise.
An agent with permission to everything is a security risk by another name. Each one gets exactly what it needs.
The one nobody wants to do and everybody does. That's where automation pays for itself and where the team welcomes it.
Which data it used and why it proposed that. Without it, when it gets something wrong there's no way to correct it or to defend it.
Customer health score, attendance anomalies, demand forecasting and purchase suggestions come with the modules, not as a separate project.
Who it is for
That takes up hours of expensive people's time every day.
They arrive as PDFs and someone keys them in.
Because looking at it costs more than deciding by eye.
Common questions
To automate tasks, no. For predictive models you do need history, and if there isn't any we say so before starting.
Yes: we work on the databases and systems you already use, without requiring you to change them.
Anything with consequences goes through human approval, and every decision leaves a record of which data it used. Without that you can neither correct it nor defend it.
It gets measured first: how many hours that task costs today. If the number doesn't add up, we say so instead of selling it to you.
Yes: customer health score, attendance anomalies, demand forecasting and purchase suggestions already come with their modules.
No. The models are applied to your data for your own operation; anything shared with an outside provider, if there is any, is disclosed beforehand.
And also
We will show you AI Automation running on a case much like yours, with no obligation.