Enterprise AI systems struggle not because the models are incapable, but because enterprise context is fragmented across catalogs, semantic layers, governance tools, lineage systems, and business metadata repositories. As organizations adopt AI agents and natural language interfaces, a fundamental challenge emerges: how can enterprise context be operationalized in a way that makes AI deterministic, explainable, and auditable?
In this session, Yogendra Sharma, Founder and CEO of Colrows, will share the engineering journey that led his team to a surprising conclusion: semantic reasoning is fundamentally a graph problem.
The session will explore how Colrows evolved from a traditional relational metadata architecture to a graph-native semantic control plane powered by Neo4j. Along the way, the team discovered that many core semantic-layer workloads, including join-path resolution, ambiguity detection, dependency management, governance enforcement, lineage traversal, impact analysis, and semantic versioning, naturally map to graph traversal and graph algorithms.
Rather than focusing on product features, the talk will examine the architectural decisions, tradeoffs, and lessons learned while building a semantic compiler for enterprise AI. Attendees will see how graph structures enable deterministic query compilation, dependency DAG management, impact analysis through reverse lineage traversal, and versioned semantic state.
You will learn practical graph-native design patterns that can be applied to semantic layers, knowledge graphs, AI agents, data platforms, and enterprise metadata systems. You will also gain insight into when graph databases become the natural choice for a workload, how to model semantic relationships effectively, and how Neo4j can serve as the semantic control plane for next-generation AI systems.
Whether you are building AI agents, data products, semantic layers, or graph applications, this session will provide concrete architectural patterns that you can apply immediately.
Speaker
Yogendra Sharma
Founder & CEO, Colrows
Yogendra Sharma is Founder and CEO of Colrows, an AI-native semantic layer that helps enterprises operationalize context for analytics and AI. With nearly two decades of experience in software engineering, data platforms, and enterprise architecture, he has led the design and development of large-scale data and analytics systems across industries. His current work focuses on semantic reasoning, graph-native architectures, and deterministic AI. At Colrows, he is building a semantic compiler that uses graphs to make AI systems more explainable, auditable, and trustworthy in enterprise environments.
