Turning your documents into usable data

The problem: the information is there, but locked in the document

A large share of administrative work consists of reading a document, extracting a few data points and copying them somewhere else. It is slow, repetitive and error-prone. According to the McKinsey Global Institute, generative AI can automate 60 to 70% of the time spent on tasks such as summarising documents or extracting information from them. It still has to be integrated into the real workflow, not left as a demo.

What AI document analysis can do

• Structured extraction: pulling the key fields (amounts, dates, clauses, references) out of an unstructured document.
• Classification and routing: automatically sorting emails, tickets and files by content, to the right department.
• Clause analysis: spotting critical clauses and deviations in contracts, surfacing risks.
• Checking and reconciliation: comparing a document against a reference (purchase order against invoice), flagging inconsistencies.
• Querying: asking a question in natural language across a body of documents, the territory of RAG.

Reliability and compliance

On high-stakes documents (legal, financial, regulated), accuracy and traceability come first. Our deployments include human validation at sensitive points, measurement of extraction quality, and sovereign hosting when confidentiality requires it. The aim is not to remove people, but to spare them the mechanical part and give them information they can verify.

Examples of application

Contract analysis and detection of risky clauses, processing of incoming files, invoice reconciliation, structuring of meeting notes. These examples illustrate common uses and do not report on a specific Kolibro engagement. A related need, centralising and querying internal knowledge, starts from the same observation: the information exists, but remains hard to use.