Machine learning models, data pipelines and automations that remove repetitive work and return information at the right time. We measure before we ship.
We start from what in your process is worth automating. We use the data you have, measure the results and put into production models that someone owns and monitors. A real example: large volumes of invoices in different formats, read without human intervention.
What it covers
- 01Document extraction and classification
- 02Machine learning applied to processes
- 03Data pipelines and reporting
- 04Agents and automations under supervision
Where we apply it
Cases where it was needed
Questions
Before writing to us
When does a language model make sense, and when not?+
It makes sense where the input is text or heterogeneous documents and an error is recoverable. Where a clear, verifiable rule exists, we use the rule. We choose starting from the cost of error.
How is accuracy measured before production?+
On a real sample of your data, against a success criterion agreed beforehand. Only with numbers on your case do we decide what to fully automate and what goes through a person.
What happens to our company data?+
They stay inside the perimeter we agree on, with restricted access and logs of what is processed. The choice of model provider depends on how sensitive the data is and is made with you.
Describe the system. We'll tell you what we see.
Ten minutes, one question at a time. At the end, our honest read of the case. It's the filter, before the call.