Anatomy of a Deep Research System - Part 1 - Architecture

Hosted by Amir Feizpour and Suhas Pai

Fri, Aug 8, 2025

4:00 PM UTC (45 minutes)

Virtual (Zoom)

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What you'll learn

how deep research systems retrieve and generate reports

what components power agentic research workflows

which design patterns shape current system architectures

Why this topic matters

In the last few months, several pioneering AI labs have launched powerful 'Deep Research' features that search extensively across a large number of data sources and produce comprehensive reports in response to user queries. In this talk, we will discuss the anatomy of such a system, focusing on the key components involved, and architectural patterns.

You'll learn from

Amir Feizpour

Founder @ Aggregate Intellect

Amir Feizpour is the founder, CEO, and Chief Scientist at Aggregate Intellect building a generative business brain for service and science based companies. Amir has built and grown a global community of 5000+ AI practitioners and researchers gathered around topics in AI research, engineering, product development, and responsibility. Prior to this, Amir was an NLP Product Lead at Royal Bank of Canada. Amir held a research position at University of Oxford conducting experiments on quantum computing resulting in high profile publications and patents. Amir holds a PhD in Physics from University of Toronto. Amir also serves the AI ecosystem as an advisor at MaRS Discovery District, works with several startups as fractional chief AI officer, and engages with a wide range of community audiences (business executives to hands-on developers) through training and educational programs. Amir leads Aggregate Intellect’s R&D via several academic collaborations.

Suhas Pai

CTO @ Hudson Labs

Suhas is the CTO & Co-founder of Hudson Labs, an NLP startup operating in the financial domain, where he conducts research on LLMs, domain adaptation, text ranking, and more. He was the co-chair of the Privacy WG at BigScience, the chair at TMLS 2022 and TMLS NLP 2022 conferences, and is currently writing a book on Large Language Models.

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