Coveo tackles AI search trust gap with context-aware relevance platform

Coveo Solutions Inc. has introduced its AI-Relevance Platform to address a persistent challenge in enterprise AI: search results and answers that lack the context, accuracy, and security controls needed to build user confidence. According to Peter Curran, chief product officer of Coveo, the problem is widespread. "Users may encounter fragmented or irrelevant search results or receive answers that lack the context or security controls needed to determine whether the information is accurate," Curran explained. The platform, available through AWS Marketplace, combines search, recommendations, and generative AI across commerce, customer service, websites, and digital workplaces to deliver grounded, reliable answers.

The platform's core capability is grounding search results in authoritative enterprise information using real-time intent signals, dynamic reranking, and continuous learning from user behavior. Curran emphasized that Coveo is not solving consumer search problems—finding a cocktail dress or choosing a paint color—but rather enabling critical enterprise work. "We're helping doctors find the right stent for an operation. We're helping companies put a hydraulic pump into a bulldozer that allows road construction to continue," Curran said. "It's not just about giving an answer. It's about giving the right answer that allows somebody to move on with their work." At the heart of this approach is the Coveo Unified Index, a cloud-based repository that ingests and normalizes both structured and unstructured information from disparate enterprise systems, creating a common framework for retrieval.

Coveo is also extending its retrieval technology to agentic AI systems through its Relevant Retrieval offering, which gives autonomous agents access to information distributed across enterprise systems while applying the context and permissions associated with that content. "We were very quick to integrate Model Context Protocol as a standard in Coveo," Curran explained. "We think about how an autonomous non-human agent would interact with the content repository that we're indexing. We've thought hard about how we give an agent the confidence that we're answering its question accurately the same way we think about how we do that for humans." The company's strategy reflects a view that enterprise search must be unified: users and AI agents should not have to navigate separate e-commerce and service portals when most questions touch both. By consolidating search and applying consistent context and security controls across human and agentic interactions, Coveo aims to make enterprise search both more efficient and more trustworthy.

Enterprise AI search has created high expectations but often disappoints: users encounter fragmented results, irrelevant answers, or information they cannot trust because it lacks proper context or security controls. Coveo addresses this gap by centering its platform on relevance, context, and security—treating enterprise search not as a simple document retrieval problem but as a problem of delivering the right answer for high-stakes work. The company's approach distinguishes between consumer search (finding a cocktail dress color) and enterprise use cases (a doctor selecting the right stent, a construction firm retrieving the correct equipment specification) where accuracy and reliability are essential.

The platform achieves this through the Coveo Unified Index, which ingests and normalizes data from disparate systems into a single repository, and through mechanisms like real-time intent signals and continuous learning from user behavior. A central insight in Coveo's positioning is that most enterprise questions touch multiple systems—an e-commerce search might also require service data—so fragmenting the search experience across separate portals undermines both efficiency and trust. By unifying the index, the platform allows a single question to surface both the right product and the right service context.

Coveo is also extending these guarantees to agentic AI, an emerging area where autonomous systems need to retrieve and act on enterprise information. By integrating Model Context Protocol and applying the same context and permission controls to agents as to human users, the company is positioning itself to support a broader shift toward autonomous AI systems in enterprise environments—while maintaining the trust and accuracy grounding that distinguishes enterprise work from consumer search.