Data without a common foundation
The same metric has a different source, meaning or calculation logic in different teams.
AI workflows over data, documents and rules
From data, systems, documents and rules we build workflows that explain deviations, check inputs and prepare the next step. When the data isn’t ready, we start there.
Starting point
You have data. A solid foundation, often not.
Reports, documents, exports and rules often live separately. Teams work with different logic, part of the know-how stays in people’s heads and exceptions are handled too late. That’s exactly where a managed workflow comes in.
Documents outside the process
Certificates, invoices, standards or internal rules are checked alongside systems, not inside them.
Know-how in people’s heads
Important context is known by specific people, but the system can’t work with it.
Exceptions caught too late
Errors and deviations are handled only once they already cost time, money or trust.
What we build
Managed workflows from data, documents and rules
We don’t build another dashboard or a generic chatbot. We connect data, documents, rules and decision points into a process you can measure, audit and grow.
We understand the process and data
We map how the work really happens, what data feeds it and what is done manually today.
We design the workflow
We define what the data layer, a rule, an integration or an AI model should handle - and where a human decides.
We build a pilot on real data
We validate the first usable version on real data, documents and exceptions.
We scale into operations
We extend the workflow to more processes, teams or documents and set up long-term maintenance.
What we actually solve
Where a workflow shows value fast
Where it’s not enough to display data or extract a document. What matters is understanding context, validating the output and preparing the next step.
Certificate and document control
Mill certificates, invoices, standards, certificates and other inputs.
DetailInvoice data extraction
Headers and line items, matching against the order and export to the ERP.
DetailWorking with standards
Relevant sections, comparison with documentation, structured outputs.
DetailQA and compliance workflow
Checklists, rules, control points and an audit trail.
Detail
From practice
From manual certificate checks to a measurable workflow
At Favex, people used to retype hundreds of certificates into the ERP by hand. Today the system extracts the data, compares it against requirements and the operator mainly handles exceptions.
The workflow handles different suppliers, multiple certificates in one PDF and multilingual documents.
4+ h
of manual work saved daily
30–45 s
average certificate processing time
95 %+
accuracy on key fields
16+
suppliers with automatic detection
The same principle works anywhere repetitive work with data, documents, rules and decisions happens.
Architecture by data sensitivity
Sensitive data never has to leave your company
Not every AI workflow should run the same way. Depending on the process we choose cloud, private infrastructure or a local model at the client.
01
low sensitivity
Cloud AI services
Fast deployment for less sensitive scenarios and pilots.
02
medium
Private cloud
Greater control over access, logs and data processing.
03
high sensitivity
Local / on-prem LLM
The model runs at the client. Data never has to leave the company.
AI that works in operations
We don’t sell technology as the goal. We build workflows meant to work in operations, not just in a presentation.
We start with the process, not the model
First we address the work, the value and the risks. Technology comes after.
We don’t wait for perfect data
When the data isn’t ready, we start by mapping sources, quality, ownership and relationships.
We’re not only over documents
We also work with data from BI, ERP and other systems.
We combine AI, rules and humans
For critical processes it must be clear who decides and why.
We’re not locked into one cloud
We choose the architecture by data sensitivity, not by a preferred API.