Illustration of a processing approach that can be adapted to your organisation and existing tools.
Knowledge is often scattered across a multitude of documents: reports, meeting minutes, technical notes, emails, procedures. Finding specific information within this collection is difficult and time-consuming with traditional tools.
Traditional search engines rely on keywords — if the user does not know the exact term used in a document, or when the same concept can be expressed in different ways, the search fails to return relevant results. AI-powered semantic search understands meaning, not just words, transforming how organisations find information.
The quantity of documents produced and stored continues to increase. Meanwhile, information is spread across multiple tools — email, shared storage, business applications, intranet — each with its own search limitations.
Documents are written by different authors with their own vocabulary and style. The same information can be expressed in many ways — a challenge that keyword-based search cannot address. Semantic search bridges this gap by indexing meaning, enabling users to find relevant documents even when their search terms differ from the document text.
Semantic search powered by AI represents a fundamental shift from keyword matching to meaning-based retrieval. Documents are indexed as vector embeddings — mathematical representations of their content's meaning. When a user searches, their query is converted to the same representation, and the system finds documents with the closest meaning.
Combined with Retrieval-Augmented Generation (RAG), this approach goes further: instead of presenting a list of document links, the system can answer questions directly with context grounded in the retrieved documents. Users get answers, not just document references, dramatically reducing search-to-insight time.
The semantic search system operates through a continuous pipeline:
1. Document indexing: documents from all sources are ingested, chunked into passages, and converted to vector embeddings 2. Vector database storage: embeddings are stored in a specialised database optimised for similarity search 3. Query processing: when a user enters a query, it is converted to a vector using the same embedding model 4. Similarity matching: the system finds document passages whose embeddings are closest to the query vector 5. Result ranking: results are ranked by relevance and presented with context snippets and source links
For RAG-enabled search, the retrieved passages are fed to an LLM that formulates a natural language answer with source citations.
Semantic document search addresses numerous practical needs:
Deploying semantic search requires attention to several factors. Index coverage determines success — if important document sources are not indexed, users will not find what they need.
Organisations adopting semantic search report transformative improvements:
A modern semantic search stack combines powerful technologies:
Q: Does the search work with all document types? R: Search can be configured to index different document types: PDFs, text documents, emails, and internal web pages. The formats effectively supported are defined according to the organisation's sources and may require specific parsers for unusual formats.
Q: Do documents need to be migrated to a new tool? R: No, search can be configured to index documents at their current location (file server, DMS, email, intranet). Documents are not moved — only their embeddings are stored in the search index.
Q: Does the search respect access rights? R: Yes, depending on the chosen configuration, search can respect existing access rights so that each user only sees documents they are authorised to access. This can be implemented at index time or query time.
Q: Can the search handle queries in multiple languages? R: Yes, multilingual embedding models can index and search documents in multiple languages. A user can query in one language and find relevant documents in another, depending on the model capabilities.
To explore complementary approaches, see: - Knowledge base — for question-answering over organisational documents - Document classification — for organising documents before indexing - Contract analysis — for semantic search within legal document corpora - Document analysis — for automated content summarisation and extraction
Every company has its own processes, constraints and tools. The examples presented on this site serve to illustrate what can be envisioned in different contexts.
