Semantic Search
Stop wasting time looking for internal documents.
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Transform Document Chaos Into Searchable Insight. Start making use of the knowledge that you have in your company. Semantic Search allows you to index office documents, PDFs, and images. Once they are indexed, you can find them using semantic search. Having this capability, you can use AI to summarize the document or extract all sorts of conclusions from it.
Key pain points
The Business Challenge
Finding Documents
Having documents in different systems makes it challenging to find them. Next to the correct search term, you also need to know the system to use for the search.
Multiple Interfaces
When using multiple systems, you need to learn how to use them. As a result, information is scattered across multiple systems, making it harder to find certain documents.
Limited search capabilities
Depending on the system that holds the documents, the search capabilities vary. Most systems allow you to search on the title of the document, but no fine-grained filtering options.
Xebia’s Semantic Search Solution accelerates knowledge discovery by removing the friction of traditional search. It indexes unstructured documents from across your systems—regardless of format or location—and makes them semantically searchable, enabling your teams to find relevant information quickly and intuitively.
Designed to scale across enterprise environments, the solution supports multiple data sources, offers advanced filtering, and integrates seamlessly with AI agents and enterprise platforms to power smarter decision-making.
Why Choose Xebia’s Semantic Search Solution?
Enterprise-Ready Scalability
Built to handle millions of documents across diverse sources and formats—without compromising speed or relevance.
System-Agnostic Integration
Connects easily to your existing document repositories, cloud platforms, and knowledge systems.
AI-Enabled Intelligence
Empowers users to go beyond search—summarize, extract, and interpret insights using AI-powered agents.
Key Features to Accelerate Growth
Multiple Sources
The Semantic Search solution supports multiple sources. The sources will be normalized and then ingested.
Vector Databases
The indexed documents are stored in full and as vectors, which means that you can find documents with words that are not literally in the document but have the same or close semantic meaning.
Facet Filtering
Semantic Search offers the ability to reduce the search results by applying facet filters. These are filters like the source system, author, document type, and any other metadata we can extract from the document's source system.
MCP Server
The indexed data in Semantic Search can be exposed through an MCP server. This allows you to integrate this solution into your current AI strategy.
AI Agent
The indexed data in Semantic Search can be exposed through an AI agent. This allows you to integrate this solution into your current AI strategy.
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