Sinequa

Sinequa is a GenAI-powered enterprise search platform that unifies content and AI to deliver secure, scalable search-powered assistants.

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EU Based EU Owned
#enterprise-search #genai #ai-assistants #knowledge-management #connectors #nlp #security #compliance #no-code #workflows #data-integration #hybrid-neural-search #grounded-ai
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About Sinequa

Sinequa is an AI-powered enterprise search platform that grounds GenAI in your own content to deliver trustworthy, search-based assistants and AI-powered workflows. It connects to your data across 200+ connectors and 350 formats, enabling secure, scalable search and automated insights across unstructured and structured content. The platform combines neural search, NLP enrichment, and orchestration to build tailored assistants and apps, with support for multiple GenAI models and a no-code builder for rapid deployment.

Key features

  • GenAI-powered enterprise search with Hybrid Neural Search and semantic ranking
  • End-to-end orchestration for multiple applications and assistants
  • NLP enrichment, entity extraction, classification, and knowledge graphs
  • 200+ connectors and 350 formats for multi-source content
  • No-code UI Builder to configure and deploy assistants and workflows
  • Model-agnostic integration with any GenAI model
  • Inline citations and provenance for trusted results
  • Centralized governance, security, and compliance (SOC 2 Type II, ISO 27001; HIPAA-compliant)
  • Scalable to hundreds of millions of documents, billions of records, and thousands of users
  • Secure, permissions-based access aligned with existing enterprise controls

Why choose Sinequa?

  • Grounded in your content to ground GenAI, reducing hallucinations
  • Flexible, scalable platform that supports multiple models and connectors
  • Enterprise-ready security and compliance with SOC 2 Type II, ISO 27001, and HIPAA controls
  • Rapid deployment with no-code builder and pre-built connectors
  • Proven leadership and customer success across industries, with centralized orchestration for multiple assistants