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Coveo Brings Essential Relevance to GenAI and Agentic AI - Introducing Coveo for Agentforce, Expanded API Suite, and a New Agentic AI Design Partner Program

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Coveo expands AI toolkit for developers with suite of off-the-shelf APIs, and launches new Agentic AI Design Partner Program to make Gen AI and Agentic AI applications smarter, faster and better

MONTREAL and SAN FRANCISCO, March 5, 2025 /CNW/ - Coveo (TSX: CVO), the market leader in AI-Relevance — delivering AI-search, generative, and business-aware relevance at every point-of-experience, today announced three advancements to augment AI, GenAI, and Agentic AI projects: Coveo for Agentforce, and expanded suite of Coveo APIs and the Coveo Agentic AI Design Partner Program.

AI-Relevance Can Fix 5 Main Challenges with AI Applications

Enterprises are drowning in data—structured and unstructured, scattered across multiple systems. AI applications, including GenAI and Agentic AI all depend on high-quality data, but most Retrieval-Augmented Generation (RAG) frameworks fall short. Basic vector databases are not enough; enterprises need advanced, hybrid retrieval and AI ranking to ensure accuracy. At Coveo, we refer to this as Relevance-Augmented Retrieval.

Another major challenge? Tech lock-in. Many AI tools are restricted to a single vendor's platform and database, often deprioritizing external content—treating it as secondary or less relevant. This creates blind spots, preventing teams from accessing the full scope of enterprise-wide knowledge. What's missing is an agnostic AI layer—one that securely connects, unifies, and enriches information from both internal and external sources without bias. Coveo bridges this gap, ensuring equal access to all relevant content, securely, no matter where it resides, whether it's structured or unstructured.

But data alone isn't enough. AI must understand each user's context, behavior, preferences and needs—personalizing every interaction not for a broad persona, but for the individual. This is the key to AI delivering relevant, meaningful experiences at scale.

To make AI projects successful in generative experiences, enterprises must overcome these four key obstacles:

  1. Accuracy and Precision – Rapidly pinpointing the most contextually relevant insights from vast amounts of structured and unstructured data—ensuring large language models receive high-quality inputs for high-precision outputs.

  2. Scattered Content and Data – Seamlessly unifying access to enterprise-wide information—eliminating silos without the need for costly content migration.

  3. Speed and Security – Delivering the right information quickly and securely—while fully respecting an organization's existing access controls.

  4. ROI and Business Impact – Delivering measurable business outcomes, not just flashy demos that overpromise and underdeliver.