Abstract
Deep Research as a consumer product redefined the AI space delivering true impact to many by searching through hundreds of websites, deeply thinking through the content, and generating a comprehensive report. In the Enterprise space, we are inundated with vast amounts of data from a multitude of diverse sources. Deeply processing and analyzing this information to uncover meaningful patterns and generate actionable insights is incredibly impactful, challenging and can be transformative to industries.
This talk will walk through how we created Deep Research for Enterprise at Glean, detailing our approach to challenges such as designing agentic systems, complex and diverse data sources, evaluation complexity, and scalability.
Main Takeaways
- How a complex Agentic AI system like Deep Research is built from start to finish.
- Strategies for handling the challenges of complex and diverse enterprise data.
- Building a robust framework for automated evaluation of agentic systems.
Interview:
What is the focus of your work these days?
The majority of my time is spent on the end-to-end cycle of creating enterprise-grade Agentic AI systems. My work focuses on designing and building novel agentic architectures and then rigorously analyzing their performance through both qualitative and quantitative data to drive continuous improvement in quality and reliability.
What was the motivation behind your talk?
While the potential of Agentic AI is incredible, my experience in the trenches building Glean's Deep Research has made one thing clear: creating these systems for the enterprise is incredibly challenging. We've had to navigate a host of complex problems, from core architecture and reliability to the nuances of building user trust. My motivation comes from being at the forefront of a new paradigm in how knowledge work gets done, and I wanted to give this talk to share the details of this journey. My goal is to provide a realistic, hands-on look at what it truly takes to build a powerful AI agent, sharing the specific, hard-won lessons we learned along the way.
Who is your session for?
ML Engineers, Engineering Leads, Data Scientists, Technical Leads, etc.
Speaker
Vinaya Polamreddi
Staff ML Engineer; Agentic AI @Glean; Previously @Apple, @Meta, and @Stanford
Vinaya Polamreddi is a seasoned ML Engineer with 8 years of experience in building impactful AI products and doing AI research. She is currently at Glean where she is the tech lead for Deep Research. Previously, she built multi-modal foundational ML capabilities at Apple to launch the Vision Pro (Apple’s 1st MR headset). Prior to that, she worked on computer vision modeling at Meta’s AI Research org, and she got her Masters in AI from Stanford.
Date
Tuesday Nov 18 / 11:45AM PST ( 50 minutes )
Location
Ballroom BC
