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Sinequa Brings the Power of ChatGPT to the Enterprise by Combining its Neural Search Engine with Generative Large Language Models

Sinequa Brings the Power of ChatGPT to the Enterprise by Combining its Neural Search Engine with Generative Large Language Models

Enterprise Search leader Sinequa announced the integration of Microsoft Azure Open AI Service into its Neural Search capabilities, putting enterprise knowledge to work like never before by combining unmatched relevance with sharp summaries and a conversational interface. This first-to-market innovation is designed to help users obtain and interact with information faster and more naturally.

Sinequa’s solution brings the power of generative AI to the workplace by pairing two powerful technologies to work together – the unmatched relevance of Sinequa’s Neural search platform and the summarization and interactivity of generative AI. This combination is key, as it provides the only way to unleash the power of generative LLMs to the enterprise safely, powering the chat with secure enterprise content thanks to Sinequa’s extensive connectivity to all enterprise sources.

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Alexandre Bilger Co-CEO at Sinequa, explained further: “The rapid emergence of ChatGPT in recent months has caused business leaders to ask how generative large language models (LLMs) can accelerate innovation and increase productivity. Combining the unmatched accuracy and relevance of Sinequa’s Neural Search with the natural language interface and answering capabilities of ChatGPT creates a powerful and first-to-market synergy that enables your employees to converse with your content”.

Generative LLMs like those powering ChatGPT are a perfect match for search because they extend how employees can interact, refine, and reformulate search results. Sinequa’s GPT summarizes the information gathered from Sinequa’s Neural Search into more rapidly digestible and reusable formats tailored to the specific needs of the employee. Sinequa’s GPT also allows an interactive dialogue, so that employees can ask deeper questions, refine the search, or refine the response. In this way, employees can converse with their content and have dialogue with their data, all using natural language.

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For example, researchers and engineers with complex questions and nuanced answers can pull relevant snippets from articles using Sinequa’s Neural Search capability and augment the results with ChatGPT to create a focused summary of the research that captures the nuances, for fast, convenient, and complete presentation of information.

Bilger concludes: “By combining the powerful capabilities of Sinequa’s Neural Search with cutting-edge generative large language models, Sinequa’s ChatGPT gives accurate, fast, actionable, and traceable answers, all in a secure enterprise environment.

“Ultimately, search is about increasing enterprise effectiveness by capitalizing on enterprise content, and combining that with generative LLMs increases the value of enterprise search by making it faster and easier to get at the information needed to be productive and accelerate innovation.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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