Author(s): Gowtham Boyina

Originally published on Towards AI.

And You Can Actually Own It

I have been watching the deep research space with a mix of admiration and frustration. OpenAI’s Deep Research is genuinely impressive, but it is a black box. You cannot inspect how it plans, you cannot swap the model, you cannot deploy it inside your own VPC, and you certainly cannot tune it for your internal knowledge base. On the other end, most open-source research agents are brittle toys. They work for a three-step Wikipedia query and fall apart on anything requiring real synthesis, citation management, or long-horizon reasoning.

After the introduction, the article explains that NVIDIA’s AI-Q is an open reference architecture for building enterprise-grade deep research agents: it uses an intent/classification orchestrator, a two-phase planner (scout then architect), and parallel specialist researchers that synthesize evidence with citation management, supported by long-context reliability strategies like avoiding raw-search context exposure. It details the “why it wins” architecture and model choices, including a fine-tuned Nemotron-3-Super, dataset generation and filtering with a judge model, and custom middleware for tool-call failures and report validation. The piece also covers optional ensemble and refiner steps to maximize quality, what AI-Q enables for enterprises, developers, and the broader ecosystem, and its limitations (setup complexity, security gaps, hardware and API dependency, benchmark-vs-reality reliability considerations, and tool/search reliance). Finally, it compares AI-Q to OpenAI Deep Research and other open or commercial alternatives, and concludes that while it’s not an easy drop-in production system today, it’s a rare open blueprint that shows how to build and measure deep research agents that organizations can own and audit.

Read the full blog for free on Medium.

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Published via Towards AI