The Forrester Wave™: Cognitive Search Platforms, Q4 2023


Sinequa was named a Leader in The Forrester Wave™: Cognitive Search Platforms, Q4 2023 report, which identifies, analyzes, and scores fourteen top vendors offering products with cognitive search capabilities across 27 different criteria.

The Forrester Wave™ report, evaluating 14 providers’ current offerings, strategies, and market presence, found:

“Sinequa continues to build on its existing innovation advantage (being one of the first to include LLMs in search) by leveraging LLMs and advanced technologies to provide deeper and more contextual search experiences.” Elaborating on this further by stating, “Sinequa is a good fit for large enterprises that have a variety of different data types, especially specific data demands such as pharma and manufacturing, and that want to deliver a highly contextual search experience that brings those data types together in multi-modal results.”

Sinequa’s not just another search company

Sinequa is also the first among these companies to offer a production-ready Retrieval-Augmented Generation (RAG) solution that works on the same principles as Microsoft’s Bing Chat and Google’s Bard with the Internet but with internal corporate documents instead, allowing its customers to rapidly leverage generative AI with their own internal content, safely and securely.

Sinequa’s platform was one of the first fully managed solutions to leverage LLMs and the first to have a production-ready integration with generative AI with Sinequa Assistant. Sinequa Assistant grounds generative AI from companies like OpenAI, Google, and Cohere with internal enterprise content, for accurate and secure use of generative AI through Retrieval-Augmented Generation (RAG). The software can be deployed on multiple public clouds, including Google Cloud, AWS, and Microsoft Azure. Our solution leverages AI technology to deliver advanced search capabilities and performance, including Sinequa’s proprietary Neural Search capability that combines keyword and vector search with numerous deep learning language models.

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We lose a lot of time searching through our various databases and existing tools, along with drawings and technical documentation. We selected Sinequa’s search engine because of its high performance and the fact that it’s straightforward to index databases.

Frédéric Antoine, Technical Support Network Manager, Airbus Helicopters