Friday, November 22, 2024

Coveo Advances GenAI with New Passage Retrieval API

Coveo, the leading enterprise AI platform that brings AI Search and Generative AI (GenAI) to every point-of-experience, announced the launch of its new Relevance-Augmented Passage Retrieval API. The new API empowers organizations to connect their own Large Language Models (LLM) with Coveo’s relevant and robust AI retrieval infrastructure to seamlessly and securely ground their GenAI experiences in the entirety of their enterprise content. Leveraging the full power of the Coveo Platform™, it retrieves the most relevant passages from multiple sources of content, securely, and delivers them in the optimal format for LLMs, ensuring the outputs are accurate, secure and firmly anchored in the most current, reliable and relevant knowledge available. As a leader in AI innovation for more than a dozen years, Coveo is now democratizing access to its industry-leading unified index, AI search and relevance capabilities, making it easier for enterprises to build their own GenAI applications with robust, industry-leading retrieval functionality.

The retrieval challenge in RAG Systems

In Retrieval-Augmented Generation (RAG) systems, enterprises can ground LLMs in their proprietary data and knowledge sources. However, the retrieval process — the “R” in RAG — is notoriously the most challenging aspect. It involves extracting the most relevant and accurate information from vast, fragmented data and content sources across the entire enterprise. This process is vital, as it directly influences the accuracy and security of AI-generated responses across all digital experiences, from websites to commerce, customer service and the workplace.

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Strengthen your GenAI applications with Coveo’s Leading Retrieval

Coveo’s new API is a powerful technology that enables enterprises to elevate their own LLM-powered applications, including, for example, making their various Copilots or other GenAI initiatives more knowledgeable, precise and relevant. It allows developers to seamlessly integrate precise, context-aware answers into their applications without the need to build a full AI search retrieval and relevance system, or the need to migrate data from multiple, siloed applications. By leveraging Coveo’s leading unified index and AI Search Relevance technology with this new API, enterprises will be able to overcome common development challenges, including generating relevant answers, ensuring accurate information across all GenAI experiences, minimizing hallucinations, maintaining secure access, respecting content permissions, and accelerating time-to-value.

Coveo’s Relevance-Augmented Passage Retrieval API will play a crucial role in an enterprise’s AI and Generative AI architecture, enhancing all digital experiences. It’s a game-changer for organizations building intelligent and high-performing large language model (LLM) applications,” said Laurent Simoneau, Co-Founder, President, and CTO at Coveo. “Enterprises using this API will benefit from our rapid AI innovation, backed by hundreds of developers and data scientists, and driven by our collaboration with some of the world’s largest enterprises in the AI search and generative experiences industry. By using our API for passage retrieval in their RAG systems, companies will be able to save time and resources, reduce risks, and ultimately achieve and surpass their business objectives faster with generative AI.”

Over 30 enterprises to date have chosen Coveo’s out-of-the-box full Coveo Relevance Generative Answering solution to solve their GenAI needs, and Coveo anticipates that a substantial portion of the market will continue to prefer to buy out-of-the-box. However, for companies that have chosen to invest in their own LLMs, they can now leverage Coveo’s Passage Retrieval API to accelerate their GenAI projects. Early adopters of Coveo’s out-of-the-box solution such as Xero, F5, SAP Concur, Forcepoint, United Airlines and many others are already reaping the benefits of Coveo’s leading GenAI technology.

SOURCE: PRNewswire

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