Your documents — PDF, Word, Excel, scans, presentations — turned into a knowledge base you query in plain language. Every answer cites the document, the page and the paragraph it came from.
In most organisations the document estate is a dormant asset: assembled, paid for, never read again.
Shared drive, mailboxes, three cloud spaces, archives, local disks. Nobody knows which version governs, and two copies of the same document circulate side by side.
Scanned PDFs, tables pasted into slide decks, annexes that amend a contract without saying so. None of it fits into a conventional database.
Some of the estate is no longer valid, and nothing flags it. Searching without separating current from expired produces confident, wrong answers.
The engine does not guess: it searches, compares, checks, then writes from what it found.
Documents are read whatever their format, scans included via OCR. Tables, annexes and attachments keep their structure. The date, version and origin of each item are retained — that is what will later separate current from expired.
The request is broken down: real intent, entities involved, constraints on date, perimeter or version. A question about “the contract in force” is not the same as “every contract signed with this supplier”.
Semantic search and exact keyword search are combined, then passages are re-ranked by actual relevance. References and article numbers, which semantic search alone systematically misses, are found.
When the relationships between pieces of information matter — linked contracts, successive versions, subsidiaries, amendments — the knowledge graph connects the entities. That is the difference between finding a document and understanding what amends it.
The answer is built from the retained passages, and each element links back to the document, page and paragraph. If sources contradict each other, the contradiction is flagged rather than silently resolved.
Direct querying is the starting point. The agentic layer is what turns search into work done.
You ask, the engine finds the information and answers with its source. “What commitments does this contract contain?” gets an answer in seconds.
Two versions, two contracts, two bids: the agent identifies the real differences, not just wording changes, and lays them out line by line.
Pulling a usable table out of a document estate: expiry dates, amounts, termination clauses, owners, in the format you impose.
The agent reads several documents, structures the information to your template and produces the note or report, with sources appended.
A Word document on your template, a slide deck, a table: the deliverable comes out in the expected format, ready to review, not to reformat.
The engine indexes and queries document estates in several languages at once, and answers in the language of the question.
“Tuned” means the algorithm itself changes with the corpus: passage weighting, chunking, version handling, date treatment. It is not a cosmetic setting.
Twelve thousand engagement documents scattered between the server and team folders. Finding a precedent takes twenty-five minutes, when you know it exists.
Document layer with the corporate profile, isolation by team, and structured extraction to assemble reference files.
The precedent is found in under two minutes, with the original deliverable and the engagement name. The estate becomes an asset again.
The contract portfolio lives in a shared space. Nobody can say, without reading manually, which contracts carry an automatic renewal clause.
Legal profile with a knowledge graph: contracts, amendments and parties linked, with version and effective-date handling.
The question is asked in one sentence and the answer arrives with the list, the articles and the deadlines. Amendments stop being forgotten.
Technical sheets, standards and calculation notes have accumulated over fifteen years, in mixed formats including many scans.
OCR over the legacy estate, catalogue profile for technical references, and structured extraction into a usable table.
Technical data becomes queryable again, including what was locked inside scanned paper documents.
The error rate is measured on your documents, with your real questions, before any industrialisation.
search time on the questions covered by the indexed corpus
of answers citing document, page and paragraph
between pilot launch and the decision to industrialise
These orders of magnitude come from what we measure at pilot on comparable perimeters. On your corpus they are measured before industrialisation, not promised before.
These products are already built. Some are available online, the others are shown on request.
The search engine that understands your documents and cites its sources.
Retrieval-augmented search coupled with a knowledge graph, tuned per theme: legal, corporate documentation, timelines. Every answer is traceable down to the original paragraph.
Try it onlineLegal research handed over to autonomous agents.
Statute and case law search, contract analysis, drafting support. Every answer points to the article and to the version in force on the date you care about.
Try it onlineNative and scanned PDFs, Word, Excel, PowerPoint, plain text, images containing text, exports from business tools. Scans go through an OCR layer before indexing.
It links entities together — a contract and its amendments, a company and its subsidiaries, a standard and its versions. Without it you find documents; with it you understand what changes their scope.
Date, version and origin are captured at ingestion, and the search profiles use them. On a legal corpus, the version in force at a given date wins. That assumes the estate can be dated — otherwise, that is the first project.
Never. The corpus stays isolated by organisation, team and user, and the agent's rights are those of the person asking.
Yes, on your infrastructure or private cloud, including with self-hosted models when corpus sensitivity requires it. That is the standard case on regulated estates.
From a few hundred to several hundred thousand documents. Beyond a certain volume the question is no longer size but the quality of the estate and the granularity of access rights.
We do not deploy a document layer on a false or obsolete estate. An excellent engine on an expired corpus produces wrong answers that are sourced and convincing — worse than no engine at all. We say so at scoping and start with the cleanup, which is a project in itself. Nor do we claim one hundred per cent reliability: we promise traceable answers and a measured error rate.
Bring a representative sample to the diagnostic. We will tell you what is usable as is, and what needs cleaning first.