Letztes Update: 04. Juni 2024
Sinequa introduces new AI assistants that integrate generative AI with neural search, elevating Retrieval-Augmented-Generation (RAG) to the next level. These assistants go beyond search and summarization, executing complex, fact-based tasks to enhance enterprise search efficiency and accuracy.
In the rapidly evolving landscape of enterprise search, Sinequa has taken a groundbreaking step by integrating generative AI assistants into their platform. This integration, known as Retrieval-Augmented-Generation (RAG), elevates the traditional search and summarization paradigm to a new level. The new assistants not only summarize search results but also execute complex, multi-step activities based on factual data.
Jean Ferré, CEO and co-founder of Sinequa, states, "We have invested heavily in artificial intelligence over the past 20 years. Thanks to our broad connectivity and high scalability, we are leaders in enterprise search. After pioneering the use of LLMs in search with specially trained small language models (SLMs), we were ready to take search and RAG to the next level with generative AI assistants."
These new assistants utilize all corporate content and knowledge to generate contextually relevant insights and recommendations while ensuring data privacy and governance. Optimized for scalability with three custom-trained SLMs, they can leverage any public or private generative LLM, including Cohere, OpenAI, Google Gemini, Microsoft Azure OpenAI, Aleph Alpha, and Mistral.ai. The result is valid answers to any internal topic, complete with citations and full traceability to the original source.
According to Gartner, generative AI will augment 30 percent of all tasks performed by knowledge workers by 2027. The key to its application in fact-based scenarios lies in its integration with enterprise search. The quality of RAG depends heavily on the supporting search capabilities. Sinequa's assistants go beyond mere summarization of search results. They execute multi-step workflows to complete complex tasks logically, incorporating RAG as needed to fully utilize the company's knowledge. This ensures that the assistant responds accurately, transparently, and securely with the most current information, including inline citations to original sources and immediate traceability—and access—to these sources.
The Sinequa assistant framework includes a range of ready-to-use assistants and tools for defining custom assistant workflows. This allows customers to deploy an assistant immediately or customize and manage multiple assistants for business scenarios through a single platform. The assistants can leverage any public or private generative LLM and can be quickly implemented and updated without code or additional infrastructure. These include:
One notable example of Sinequa's generative AI integration is TotalEnergies' implementation of the Sinequa assistant JAFAR (Jenerative AI for Availability REX). This assistant helps TotalEnergies better utilize feedback following production incidents in their refineries. Aude Giraudel, Head of Smart Search Engines at TotalEnergies, explains, "The new search app simplifies finding information in TotalEnergies' knowledge databases. It is based on the Sinequa search engine/RAG combined with generative AI and improves decision-making by analyzing documents and providing recommendations."
Sinequa's generative AI assistants are designed with data privacy and governance in mind. They ensure that all generated insights and recommendations are based on the most current and accurate information available. This is achieved through the use of inline citations and full traceability to the original sources. By leveraging both public and private generative LLMs, Sinequa ensures that their assistants can provide valid answers to any internal topic while maintaining the highest standards of data privacy and governance.
One of the key strengths of Sinequa's generative AI integration is its scalability and customization. The platform is optimized for scalability with three custom-trained SLMs, allowing it to handle large volumes of data and provide accurate insights and recommendations. Additionally, the framework includes tools for defining custom assistant workflows, enabling customers to tailor the assistants to their specific business needs. This flexibility ensures that Sinequa's generative AI assistants can be quickly implemented and updated without the need for additional infrastructure or coding.
As generative AI continues to evolve, its integration with enterprise search platforms like Sinequa will play a crucial role in enhancing the capabilities of knowledge workers. By leveraging the power of RAG, Sinequa's generative AI assistants are able to provide contextually relevant insights and recommendations, improving decision-making and productivity across a wide range of industries. With their focus on data privacy and governance, scalability, and customization, Sinequa is well-positioned to lead the way in the future of enterprise search.
In conclusion, Sinequa's integration of generative AI assistants into their enterprise search platform represents a significant advancement in the field. By combining the power of RAG with the capabilities of generative AI, Sinequa is able to provide accurate, transparent, and secure insights and recommendations. This integration not only enhances the capabilities of knowledge workers but also ensures that data privacy and governance are maintained. As generative AI continues to evolve, Sinequa's innovative approach to enterprise search will undoubtedly play a crucial role in shaping the future of the industry.
The integration of generative AI assistants into enterprise search by Sinequa marks a significant advancement in the field of information retrieval. This development leverages cutting-edge AI to enhance user experience and efficiency in accessing relevant data within corporate environments. As businesses continue to adopt innovative technologies, the role of AI in streamlining operations and improving decision-making processes becomes increasingly crucial.
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