Naresh Dulam
Vice President of Software Engineering
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Naresh Dulam is the senior vice president of Software engineering at JPMorgan Chase & Co., with an extensive background in data architecture, cloud solutions, and AI-powered analytics. Over his career, he has held pivotal roles across prominent organizations like Health Care Service Corporation, Bank of America, ATT, T-Mobile, Oracle India, and Infor, where he has leveraged expertise in big data, cloud computing, and machine learning to build innovative data platforms and pipelines in sectors including Financial Services, Healthcare, and Supply Chain.
Improving RAG Systems - Key Challenges and Solutions
RAG systems combine data retrieval with language generation, but they face significant issues like missing content, context mismatches, and retrieval inaccuracies, leading to hallucinations and incomplete responses. Solutions include advanced data cleaning, improved prompting, agentic RAG models with live search, and refined retrieval techniques, such as hyperparameter tuning, chunking strategies, and enhanced embedding models.
Additionally, it covers corrective approaches like multi-query retrieval, context compression, reranking, and recent research-driven methods for reducing hallucinations and achieving higher response specificity. The document emphasizes future directions with agentic RAG systems and self-reflective models, paving the way for robust RAG deployments in high-stakes applications.
Improve Agentic RAG Systems - Key Challenges and Solutions
RAG systems combine data retrieval with language generation, but they face significant issues like missing content, context mismatches, and retrieval inaccuracies, leading to hallucinations and incomplete responses. Solutions include advanced data cleaning, improved prompting, agentic RAG models with live search, and refined retrieval techniques, such as hyperparameter tuning, chunking strategies, and enhanced embedding models. Additionally, it covers corrective approaches like multi-query retrieval, context compression, reranking, and recent research-driven methods for reducing hallucinations and achieving higher response specificity. The document emphasizes future directions with agentic RAG systems and self-reflective models, paving the way for robust RAG deployments in high-stakes applications.
From Reactive to Real-Time: How AI Agents are Powering the Future of Sustainability
As enterprises face mounting pressure to meet environmental, social, and governance (ESG) goals, traditional approaches to sustainability fall short—often reactive, slow, and overwhelmed by data. In this session, we explore how AI agents are transforming sustainability efforts from static monitoring to intelligent, autonomous decision-making.
We’ll break down the fundamentals of AI agents—what they are, how they work, and why they matter. Learn how these autonomous systems can continuously observe environments, interpret complex data, and take meaningful actions to drive ESG performance. From optimizing energy in green buildings to automating real-time compliance reports, AI agents are becoming the backbone of sustainable enterprises.
Whether you're a business leader, technologist, or sustainability advocate, this talk offers a practical and forward-looking lens on how to embrace the agentic future and turn sustainability into a proactive, data-driven advantage.
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