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Enterprise Layer Intelligence
Categories/Knowledge Management/Sinequa
Sinequa

Sinequa

Founded by Alexandre Bilger & Jean Ferré in 2002

Search internal company documents and connect agents to enterprise knowledge

Pricing model

Custom

You can use Sinequa to connect, search, and analyze large volumes of internal enterprise data across diverse storage systems. The system indexes hundreds of millions of documents, including unstructured text and complex technical files, while enforcing strict user permissions. Employees can ask direct questions in natural language to retrieve accurate answers alongside source citations. Organizations can also deploy custom automated assistants and agents that ground their actions in corporate knowledge, preventing fabricated responses and making internal information easily accessible across departments.

What Sinequa does

Index internal files and corporate databasesQuery enterprise documents using natural languageBuild custom search assistants for specific departmentsFilter search results based on user permission levelsLink verified internal documentation to autonomous agentsExtract technical data from complex specificationsConnect to over 200 enterprise data sourcesEnforce document-level security and user access permissionsIndex hundreds of millions of technical and unstructured documentsGround AI assistants directly in verified company dataBuild custom assistants using a no-code configuration builderAutomate content enrichment and metadata extraction

Pricing breakdown

Pricing model: Custom

Frequently asked

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Sinequa, enterprise search, enterprise AI, AI agents, retrieval augmented generation, RAG, knowledge discovery, document search, enterprise assistants, unstructured data, semantic search, data connectors, internal knowledge, natural language query, document indexing