AI agents for classification, extraction, semantic search, summarisation and the automation of content operations.
Routine work on documents
In an organisation that receives thousands of documents, much of the effort goes into operations that need no judgement: recognising what kind of document it is, pulling a few fields out of it, sending it to the right department, summarising it for someone with no time to read it all.
These are exactly the operations an AI agent can do well, provided it is kept on a leash: working on the organisation’s documents, not on general knowledge, and leaving a trace for every decision.
What the agents do, concretely
Classification places the document in a category of the organisation’s nomenclature, not a generic one. Extraction pulls out the fields that matter for that type — parties, dates, values, deadlines — and proposes them for completing the entry.
Semantic search finds documents by meaning, not by exact word match: a search for “terminating a supplier contract” also reaches the document that talks about “ending the contractual relationship”, if that is what it is about.
Summarisation produces a summary of a document or a file, for someone who has to decide quickly. The summary stays linked to the source, so it can be checked; it does not replace the document.
- classification in the organisation’s own nomenclature;
- extraction of the fields relevant to the document type;
- search by meaning, not by exact word match;
- summaries linked to the source document, verifiable.
Where the agent stops
The level of autonomy is configured per type of operation. Proposing a classification can be automatic; registering a document in a register with legal consequences can require a person’s confirmation. The rule is not technical, it is internal policy.
Every operation of an agent goes into the audit log just like one done by a person: what it did, when, on which document. The question “why did the document end up there” has an answer.
Real archives are not monolingual
An institution working with European funds has documents in Romanian and English. One with foreign business relations adds French, German, sometimes others. Even without an international context, the archive holds technical documents in English and correspondence in Romanian, in the same file.
A system that handles only one language well turns the rest into opaque files: they can be stored, but not found, classified or summarised.
What multilingual handling means
Classification recognises the document type whatever language it is written in and places it in the same category of the nomenclature. A contract stays a contract, whether it says “contract” or “agreement”.
Search crosses languages: a query in Romanian can find a document in English, if it is about what is being searched for. For a mixed archive, this is the difference between finding and not knowing it exists.
Field extraction and summarisation work on the same logic, and the summary can be produced in the organisation’s working language, whatever the language of the source.
- classification independent of the document’s language;
- search that crosses languages;
- field extraction from documents in different languages;
- summaries in the organisation’s working language.
What the organisation gains
The archive becomes uniform in use, even if it is not uniform in language. There is no longer a “rest of the documents” nobody searches because they cannot.
And for externally funded projects, where bilingual documentation is the rule, the work of keeping two parallel records disappears.
Questions and answers
Do the agents learn on our documents?
The way of working — what data is used, where it is processed, what is kept — is set at implementation and follows the organisation’s data sovereignty policy.
What happens when the agent gets it wrong?
Its proposal can be corrected by the operator, and the correction stays on record. For operations with consequences, human confirmation can be made mandatory through configuration.
Does the summary replace reading the document?
No. It stays linked to the source and serves for quick orientation, not for deciding without checking.
Does it work on documents in several languages?
Yes, see the Multilingual AI capability for details.
Which languages are covered?
The actual coverage depends on the models used in the implementation and is set according to the organisation’s needs.
Does it translate documents?
Classification, search and summarisation work across languages. Full translation of documents is a separate requirement, discussed if you need it.
Can the summary of an English document be in Romanian?
Yes, the language of the summary can be chosen independently of the language of the source.
Does it work on scanned documents?
Yes, to the extent their content was extracted at capture.