Devraj Sanyal

Devraj Sanyal

Co-Founder and CTO

dōnō consulting

Devraj Sanyal is co-founder and CTO of dōnō consulting. A leader in the technology space with a particular passion for implementing algorithms at scale, over the past 25 years Dev has worked across tech stacks, geographies, programing languages and from small organizations to global giants. Most recently he was a VP at Goldman Sachs, prior to which he spent a decade at Thomson Reuters in Bangalore and New York, with stints in London and Beijing. He also worked for Astral Systems, HCL and CA. Dev was one of the earliest people working on Knowledge Graphs (KG) in NLP/AI in India, when he developed and implemented OWL solutions for financial data in one of the largest global investment banks and real time sentiment analytics at one of the largest financial data providers in the world. He is now implementing KG with LLMs for clients at dōnō consulting, and for skilling.ai and AiUrGraph, two products being developed by the firm.

Along with helping clients with complex AI and ML problems and implementing those solutions at scale, Dev is also a much sought after corporate educator, working with leaders and technologists across industries and domains. Recently he was also a faculty at the IIT Delhi CTO Program.

Large Language Models (LLMs) are transforming AI, but they face critical challenges in grounding, memory, and reasoning. This session explores how graphs (from knowledge graphs and ontologies to graph databases and graph neural networks)address these gaps and are essential for the future of LLMs. We begin with core concepts and academic foundations, then move into practical applications in search, recommendations, and reasoning. Global case studies and live examples from skilling, healthcare, and legal domains will be shared. Participants will also learn about key commercial and open source tools like Neo4j and Janus, the role of ontologies, and how LLMs are making ontology creation faster and easier.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More