For years, enterprises have struggled with fragmented knowledge. Information is spread across multiple systems — CRMs, intranets, wikis, document repositories — forcing employees and customers to dig through multiple disconnected sources just to find a single answer. The result? Frustration, wasted time, and operational inefficiencies.

Generative AI, or Gen AI, promised to solve this problem by making knowledge retrieval faster and more intuitive. The idea was simple: Ask artificial intelligence a question, and it generates the perfect generative AI output.

Despite its ability to generate human-like responses, many organizations quickly realized that a large language model (LLM) is only as good as the input data it retrieves. Without proper data preparation and retrieval governance, generative AI tools can produce inaccurate, misleading, or even risky AI output.

For enterprises, this has led to serious challenges:

  • Wrong or misleading generative AI outputs: When AI pulls from outdated, unverified, or low-quality sources, it produces wrong answers or hallucinations (answers that sound convincing but are completely false).
  • Security risks: Without strict access controls, AI can surface internal or confidential documents to the wrong users.
  • Inconsistent AI performance: Even when AI delivers correct answers, organizations lack insight into why a particular response was generated or how to improve it over time.

To truly harness retrieval-augmented generation (RAG) for enterprise knowledge management, organizations need a platform that ensures visibility, security, and control over AI-generated knowledge.

That’s why we built Coveo’s Knowledge Hub.

An image illustrates how Coveo's Knowledge Hub reports on generative AI output

Why Is Controlling the Output of Generative AI Systems Important?

Deploying generative AI without clean, current data and governance is like trying to build a house on a weak foundation. Generative models might retrieve and generate answers, but without proper oversight, those answers may be inaccurate, incomplete, or even harmful to business operations.

Imagine a customer support agent using an AI-powered assistant to help resolve a complex inquiry. Without AI governance and structured retrieval, several things could go wrong:

  1. The generative AI model pulls from outdated product documentation, providing the wrong troubleshooting steps to the customer.
  2. A confidential internal strategy document appears in the GenAI output, creating a major compliance violation.
  3. The AI tool retrieves conflicting information from multiple sources, leaving the human agent uncertain about which answer is correct.

These failures aren’t just inconveniences — they can damage trust, create legal and security risks, and ultimately undermine the entire generative AI investment.

To prevent these issues, enterprises need a way to review what documents or chunks are used in the generated output, ensuring AI retrieves accurate, secure, and well-governed knowledge.

That’s what Coveo’s Knowledge Hub is built for.

Coveo’s Knowledge Hub: AI Knowledge Retrieval, With Full Control

Making AI Answers Fully Traceable

One of the biggest frustrations with generative AI technology is lack of transparency. Users see an AI output, but they don’t know where it came from or why it was generated. Coveo’s Knowledge Hub eliminates this black-box problem by providing full AI answer traceability.

Organizations can:

  • See exactly which documents and passages the generative AI system retrieved for a given response.
  • Audit AI-generated content to ensure accuracy, compliance, and reliability.
  • Identify content gaps where AI lacks sufficient, high-quality information to generate the best answer.

With this real-time visibility, enterprises don’t just get AI generated output — they get data-driven insights that improve content governance over time.

An animation shows the different analysis options

Keeping Gen AI Tools Retrieval Secure and Compliant

Enterprise knowledge contains highly sensitive data, from legal contracts to HR policies, financial reports, and internal strategy documents. Without strict access controls, an AI system can accidentally surface restricted content, putting organizations at risk of:

  • Regulatory non-compliance.
  • Intellectual property exposure.
  • Data privacy breaches.

Coveo’s Knowledge Hub integrates advanced security and compliance measures, ensuring that:

  • AI retrieval follows enterprise-wide security policies.
  • Document-level permissions are enforced, meaning AI only retrieves content that users are authorized to access.
  • Confidential and sensitive data remains protected, even when used in AI-powered knowledge retrieval.

With built-in governance and security, enterprises can confidently deploy GenAI without compromising data integrity.

Relevant reading: Preventing Data Leaks: Strategies for Secure Enterprise AI

An image visualizes how business rules can be added to the knowledge hub to adjust AI output

Preventing AI From Generating Incorrect Responses

Generative AI does more than just retrieve knowledge — it creates new responses based on what it finds. If left unchecked, AI can misinterpret incomplete, ambiguous, or conflicting content, leading to misleading or entirely fabricated responses.

Coveo’s Knowledge Hub ensures real-time answer control, allowing organizations to:

  • Block incorrect or misleading AI-generated responses before they reach users.
  • Refine AI retrieval settings to prioritize high-value, trusted content sources.
  • Fine-tune AI ranking and scoring models, ensuring that AI prioritizes the most relevant, up-to-date knowledge.

This hands-on control prevents misinformation from spreading and ensures that AI always provides fact-based, enterprise-approved responses.

Real-time Feedback to Continuously Optimize Answer Quality

AI isn’t a set-it-and-forget-it solution — it requires continuous learning and refinement. Without an improvement loop, Gen AI tools can drift toward lower-quality answers over time.

Coveo’s Knowledge Hub enables enterprises to:

  • Collect real-time feedback on AI-generated responses, allowing employees and customers to flag incorrect or unhelpful answers.
  • Use machine learning to optimize content ranking and retrieval models, making AI more precise with each interaction.
  • Empower subject matter experts to review AI generated work, ensuring that the system evolves based on business needs and domain expertise.

With this continuous learning approach, AI-powered retrieval becomes more valuable the more it is used, instead of degrading in accuracy over time.

Transforming Enterprise AI Knowledge Management

Coveo’s Knowledge Hub offers a comprehensive platform crafted to ensure generative AI output is traceable, secure, and continuously improving. For enterprises grappling with AI accuracy, governance, and security, it offers unparalleled solutions:

  • Transparency in AI-generated output: Facilitating trust and verification in AI knowledge retrieval.
  • Enterprise-grade security and compliance: Preventing unauthorized access to sensitive data during AI output retrieval.
  • Real-time control and intervention of responses: Empowering organizations to block, refine, and optimize AI-generated content.

By ensuring AI-generated content is precise, structured, and secure, enterprises can confidently implement AI-powered knowledge retrieval systems.

What’s Next?

The future of enterprise AI isn’t about simply retrieving information — it’s about delivering the right answer, at the right time, with full transparency and security.

With Coveo’s Knowledge Hub, organizations can finally bridge the gap between AI-powered search and enterprise knowledge governance — unlocking a new era of trusted, scalable AI knowledge management.

Want to see Coveo’s Knowledge Hub in action? Get a demo today and learn how it can transform AI-powered knowledge retrieval for your enterprise.

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