Smarter AI Starts Here: Why DeepQuery Is the Next Step in GenAI Maturity
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In the fast-moving world of business, getting the right answer quickly can mean the difference between seizing an opportunity and missing it altogether. We’ve recently seen a surge in the use of generative AI (GenAI) to power chatbots, streamline support, and answer fact-based questions in seconds. But as adoption matures, a new challenge is emerging: not just knowing what happened — but why. Today, we’re excited to introduce Denodo DeepQuery a major step forward in enabling GenAI to deliver not just responses, but real understanding.

What Is DeepQuery — and Why Do We Need It Now?

At a high level, Deep Research refers to a new class of AI capabilities powered by reasoning models — large language models (LLMs) designed to investigate, synthesize, and explain. Unlike traditional models that simply retrieve and rephrase facts, reasoning models go further: they analyze complex questions, search across multiple systems and sources, and deliver well-structured, explainable answers.

While this idea has been explored in academic circles for years, recent advances in model architecture and retrieval-augmented generation (RAG) have made enterprise-grade Deep Research not just possible but practical. And we believe this marks the next phase of AI in the enterprise.

How is Deep Research Different from Traditional LLMs (Even with RAG)?

Most of today’s enterprise GenAI use cases are powered by traditional LLMs combined with retrieval-augmented generation (RAG). These are great for fast lookups — pulling documents or data queries in response to questions like “What was our revenue last quarter?” or “When did we launch this product?”

Deep Research takes this further. Much further.

Instead of retrieving isolated facts, Deep Research:

  • Breaks down complex questions into smaller, searchable parts
  • Leverages data from multiple sources (e.g., sales, finance, HR, and call center logs)
  • Synthesizes insights across data products and documents
  • Draws conclusions with citations and transparent reasoning paths

In short, it doesn’t just surface what’s happened — it explains why it happened, what’s driving the trend, and what other factors might be involved. This is the kind of analysis that would normally take a skilled analyst days to complete. Deep Research can do it in minutes.

Why Organizations Need Both: Traditional LLMs and Deep Research

You might ask, “If Deep Research is so powerful, why not use it for everything?” It’s a fair question — but like most advances in enterprise IT, it comes down to whether or not it’s fit for purpose.

Traditional LLMs — with or without RAG — are well-suited for:

  • Quick answers and summaries
  • Drafting content
  • Conversational BI
  • High-volume tasks

Deep Research is better suited for:

  • Investigations into root causes and patterns
  • Cross-functional analysis that spans data sources
  • Open-ended questions that require reasoning, synthesis, or explanation
  • Situations in which it’s acceptable to take more time to address complex questions

Think of traditional LLMs as the AI equivalent of a fast search engine or helpful assistant. Deep Research is more like a seasoned analyst — connecting the dots, validating findings, and delivering narrative intelligence that can drive decisions.

Now Available: DeepQuery in the Denodo Platform

We’re proud to announce that DeepQuery is now available as part of the Denodo AI SDK. If your organization is already using Denodo’s Query RAG capabilities, this adds a powerful new dimension to your GenAI toolkit. And if you haven’t started using the Denodo Platform for GenAI yet — no problem. It’s fast and easy to get up-and-running.

While traditional GenAI excels at quick, fact-based questions like “What’s the balance in this account?” or “What’s our churn rate in the Northeast?” DeepQuery is built for the next level — enabling you to ask complex, analytical questions such as:

“Why is churn increasing in the Northeast, and what factors — customer behavior, service response time, or competitive activity — are contributing?”

To answer these kinds of open-ended, high-value questions, GenAI needs more than static documents or siloed data — it needs live, governed access to a broad spectrum of sources. That’s where the Denodo Platform makes a real difference. Denodo DeepQuery taps directly into your existing enterprise systems — across departments, formats, and data types — without creating copies or requiring new pipelines.

The result? Richer insights, more accurate conclusions, and faster time-to-action — because your AI isn’t limited by where the data lives or how it’s stored. It’s free to reason across your business, just like a seasoned analyst would.

That’s the difference between simply knowing what happened — and truly understanding why, so your teams can act with clarity and confidence.

Driving Business Value Beyond the Chatbot

If your AI initiatives are focused solely on chatbots or surface-level Q&A, you’re likely missing out on the deeper, more strategic benefits of GenAI.

With DeepQuery, You Can:

  • Accelerate decision-making by giving business users explainable insights in minutes
  • Reduce analyst bottlenecks and empower teams to answer their own cross-functional questions
  • Eliminate shadow pipelines by operating directly on real-time, governed data
  • Build trust in AI-driven answers through citations and transparent reasoning
  • Support advanced use cases like competitive intelligence, policy risk assessment, and customer journey analysis — all powered by real-time enterprise data

This is GenAI not as a chatbot, but as a strategic advisor — helping your teams go from question, to insight, to action, faster than ever.

Ready to Explore DeepQuery?

Denodo DeepQuery is now available in private preview as part of our AI Accelerator Program. If your organization is exploring GenAI beyond the basics and wants to unlock richer, more explainable answers from your enterprise data, we’d love to hear from you.

Let’s go beyond the what. Let’s get to the why.

Kevin Bohan
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