NemoFlow Studio

Inspiration

Traditional AI assistants often provide answers without clear evidence or transparency. We wanted to build a system that doesn't just generate responses but actually researches information, verifies sources, and explains how conclusions were reached. Our goal was to create an AI-powered research workspace that helps users make confident, evidence-backed decisions.

What it does

NemoFlow Studio is a multi-agent AI research platform that transforms complex questions into cited, evidence-backed reports. It uses NVIDIA Nemotron models to plan research, gather information through live web search, extract evidence, synthesize insights, and review findings before presenting a final report. The platform provides source citations, workflow transparency, telemetry metrics, and visual diagrams to help users understand both the answer and the reasoning behind it.

How we built it

We built NemoFlow Studio using Next.js, React, TypeScript, Tailwind CSS, and Framer Motion. NVIDIA Nemotron models hosted through Nebius Token Factory power the multi-agent workflow, with Nano planning tasks, Super extracting and reviewing evidence, and Ultra generating the final report. Tavily Search provides real-time web research and source retrieval. The application streams workflow updates through Server-Sent Events (SSE) and stores recent research sessions locally in the browser.

Challenges we ran into

One of the biggest challenges was coordinating multiple AI agents while maintaining consistency across the research pipeline. We also had to manage source deduplication, citation tracking, error handling, streaming updates, and model fallback mechanisms. Ensuring that information remained grounded in real sources while keeping response times reasonable required careful workflow design.

Accomplishments that we're proud of

We're proud of building a complete multi-agent research system that goes beyond a traditional chatbot. The platform provides transparent workflows, cited reports, evidence reviews, visual diagrams, and performance telemetry. We successfully integrated multiple NVIDIA Nemotron model tiers into a single collaborative research process and created a polished user experience that makes advanced AI research accessible.

What we learned

Through this project, we learned how to orchestrate multiple AI agents to solve a single problem more effectively than a standalone model. We gained experience with prompt engineering, retrieval-augmented workflows, real-time streaming architectures, citation management, and optimizing AI model routing for both performance and cost.

What's next for NemoFlow Studio

We plan to expand NemoFlow Studio with document uploads, PDF and spreadsheet analysis, collaborative workspaces, persistent cloud storage, enterprise knowledge-base integrations, advanced fact-checking, customizable agent workflows, and richer visual analytics. Our long-term vision is to create a trusted AI research environment for professionals, students, developers, and organizations.

Built With

• framermotion
• next.js
• react
• tailwindcss
• typescript