THE INSIGHT ENGINE
Have a question?
How does Denodo reduce "hallucinations" in RAG-based Agentic AI?
Hallucinations often occur because of stale or context-poor data. Denodo provides the most current, governed enterprise data to the LLM context window, ensuring the AI reasons based on facts, not outdated snapshots.
Does Denodo support the integration of unstructured data (PDFs/Logs) for AI workflows?
Yes. Denodo can join structured data with with semi-structured and unstructured content, providing a unified "Knowledge Graph" for your AI models to consume.
How do we ensure our AI agents are compliant with data residency laws (e.g., GDPR)?
Denodo enforces data residency through centralized governance and attribute-based access controls. It evaluates who is requesting data, where the request originates, and data sensitivity. Based on these attributes, it dynamically restricts, masks, or permits access, ensuring consistent compliance with data sovereignty requirements across all users and AI agents.
How do we ensure AI agents retrieve only relevant data for each task?
Denodo applies semantics and governance to filter and deliver only relevant data, improving decision quality and reducing noise.
How do data products support AI and machine learning workflows?
Data products provide clean, semantically consistent, and governed data that can be directly consumed by AI models.
How does a Logical layer speed up data preparation for Machine Learning?
Data scientists often spend up to 80% of their time preparing data. Denodo delivers well-curated, pre-integrated, high-quality, and pre-governed data views, enabling them to access clean, ready-to-use features directly in their notebooks without manual data preparation, accelerating model development and time to value.
How do AI developers consume data without understanding underlying systems?
Data products provide ready-to-use, governed data services, allowing developers to focus on AI logic rather than data integration and preparation.
How do data products accelerate moving from AI prototype to production?
By providing governed, ready-to-use data services, data products eliminate repeated data preparation and integration. They also prevent logic from being embedded in individual agents, reducing rework and agent proliferation, making it easier to scale consistently from prototype to production.
How do we update the data sources feeding our AI agents without having to retrain the models?
Since the AI agent connects to a virtual "Product" or "Customer" entity in Denodo, you can swap the backend source (e.g., from an on-prem DB to a cloud Lakehouse) without changing the data contract the AI relies on.
Is there a better way to avoid agent proliferation and repeated rework when connecting agents to each data source?
Yes. A logical data layer provides unified, reusable access across sources, reducing duplication while standardizing semantics and governance. This enables agents to scale efficiently, minimizes rework across applications, and ensures consistent, trusted outcomes without complex, point-to-point integrations.