Semantic search across document bases

A document found in 10 seconds

Keyword search means guessing the vocabulary the author used. Employees rephrase, scroll through pages of results and end up asking a colleague, which takes two people to find a file that already exists.

The automated workflow

A structured process for fast, reliable results.

Semantic indexing

Your document base is ingested and vectorised so that content is understood beyond the exact words.

Plain-language query

Employees phrase their search as a question or a description of what they need.

Similarity search

Relevant documents are found by closeness of meaning, even when the vocabulary differs.

Enriched results

Documents ranked by relevance, with contextual excerpts and the key passages highlighted.

Tangible results

10 s

Average time to find a document

-50%

Requests to colleagues to find a file

Plain language

Search by meaning rather than exact words

Up and running in 4 to 6 weeks

From specification to deployment, with visible results from the first few weeks.

Rapid scoping (1 week)

Working prototype at the halfway point

Deployment and training included

Frequently asked questions: Semantic search across document bases

A conventional engine looks for exact keyword matches. Semantic search interprets the meaning of the query and finds relevant documents even when they use other words.

Yes, scanned documents first go through optical character recognition, then join the semantic index. Scan quality determines extraction accuracy.

The vector search architecture handles bases of several hundred thousand documents while keeping response times short.

Yes, the engine applies the permissions defined in your document management system: each user sees only the documents they are authorised to access.

More workflows: Document Management

50 pages summarised in 2 minutes

Summarising long documents

Annual reports, sector studies and technical manuals far exceed the reading time decision-makers have. Useful information stays inside the document, and decisions are made on what people believe it contains.

3 to 4 weeks

Discover

-65% extraction time

Structured knowledge extraction

A company's know-how flows past continuously: internal memos, minutes, messages, reports. Feeding a wiki or a reference base means rereading that material and turning it into entries, a job nobody ever finishes, so expertise leaves with the people who hold it.

4 to 6 weeks

Discover

-30% document volume

Duplicate detection and document consolidation

Over the years, a document base accumulates copies, outdated versions and redundant content. That volume weighs on storage, dilutes search results and exposes the organisation to the costliest risk of all: working in good faith on an obsolete version.

3 to 5 weeks

Discover

See all workflows: Document Management