WORKSHOP
Everyone Has Models, Nobody Has Context: Designing Enterprise-Ready AI Systems with Context Graphs (WS02)
Date: 07 October 2026 | Time: 11:30-02:00 PM
Venue: Workshop Room 2, NIMHANS Convention Centre, Bangalore
FEES:
• Rs.99 for Leaders Pass holders
• Rs.669 for Professionals Pass holders
• Rs.749 for Knowledge Pass holders
• Rs.899 for Community++ or Student Pass holders
• Rs.999 for all others
(Limited seats available)
The objective of this workshop is to help participants understand why AI models alone are insufficient for delivering enterprise value and how enterprise context becomes the key differentiator in Agentic AI systems.
Through a combination of concepts, architecture patterns, and a hands-on demonstration, participants will learn how to design and implement a Context Layer that enables AI agents to reason across enterprise systems, understand business semantics, and generate reliable, explainable outcomes.
By the end of the workshop, attendees will be able to design an enterprise intelligence architecture that combines data, context, and agents to solve complex cross-functional business problems.
Karan Chellani
Senior Architect | GenAI & Enterprise AI Solutions Leader, Persistent Systems Ltd.
- This workshop is suitable for:
- Enterprise Architects
- Solution Architects
- Data Architects
- AI/ML Architects
- GenAI Engineers
- Data Engineers
- Knowledge Graph Practitioners
- Platform Engineers
- CTOs and Technology Leaders
- Product Managers working on AI products
- Innovation Teams
- Digital Transformation Leaders
- Technical Consultants and Researchers
Part 1: The Enterprise AI Challenge
- Why models are becoming commodities
- Limitations of traditional RAG architectures
- Why enterprise AI projects struggle at scale
- The missing context problem
Part 2: Understanding Enterprise Context
- What is enterprise context?
- Business semantics and meaning
- Ontologies and taxonomies
- Institutional knowledge
- Process intelligence
- Decision history and governance
Part 3: From Data Fabric to Context Fabric
- Evolution from Data Warehouses to Agentic AI
- Why Data Fabric alone is insufficient
- Context Layer architecture
- Agent-readability vs Human-verifiability
Part 4: Context Engineering Patterns
- Knowledge Graphs
- Context Graphs
- Semantic Models
- Ontology-driven systems
- Metadata-driven intelligence
- Open Knowledge Formats (OKF)
- Part 5: Agentic AI Architecture
- Context Layer
- Agent Harness
- Tool Calling
- MCP Integration
- Multi-agent orchestration
- Governance and security considerations
- Part 6: Hands-on Demonstration
- Live Demo: Building an Enterprise Context Layer
Example Scenario:
- BigQuery Financial Data
- ServiceNow Support Data
- Context Graph Layer
- AI Agent Reasoning Across Systems
Participants will see:
- How entities are mapped
- How business relationships are created
- How AI agents discover context
- How cross-domain reasoning works
- How business questions are answered without duplicating data
Part 7: Implementation Roadmap
- Getting started quickly
- Building a minimum viable context layer
- Choosing between Wiki, Graph, and Knowledge Platforms
- Scaling towards enterprise-wide adoption
After attending this workshop, participants will be able to:
- Identify why many enterprise GenAI initiatives fail despite having access to powerful LLMs.
- Distinguish between Data, Knowledge, Context, and Intelligence layers.
- Design a Context Layer architecture that enables AI agents to reason rather than merely retrieve information.
- Connect siloed enterprise systems through semantic relationships without copying data.
- Apply Context Engineering principles to existing RAG and Agentic AI solutions.
- Evaluate when to use Knowledge Graphs, Ontologies, Metadata Catalogs, or Context Graphs.
- Architect enterprise AI platforms that are:
- Explainable
- Governable
- Scalable
- Agent-ready
- Create a roadmap for implementing context-aware AI in their own organizations.
Benefits/Takeaways of this workshop for the attendees (What will attendees do after attending the workshop which they were not able to do before attending this)
After attending this workshop, participants will be able to:
- Identify why many enterprise GenAI initiatives fail despite having access to powerful LLMs.
- Distinguish between Data, Knowledge, Context, and Intelligence layers.
- Design a Context Layer architecture that enables AI agents to reason rather than merely retrieve information.
- Connect siloed enterprise systems through semantic relationships without copying data.
- Apply Context Engineering principles to existing RAG and Agentic AI solutions.
- Evaluate when to use Knowledge Graphs, Ontologies, Metadata Catalogs, or Context Graphs.
- Architect enterprise AI platforms that are:
- Explainable
- Governable
- Scalable
- Agent-ready
- Create a roadmap for implementing context-aware AI in their own organizations.
Basic understanding of:
- GenAI
- LLMs
- RAG
Credentials on NAMS(Neo4j Agent Memory Service), OPENAI KEY
About Speakers
Karan Chellani is a Senior Architect with nearly two decades of experience in Enterprise Architecture, Cloud Transformation, Data Platforms, Artificial Intelligence, and Generative AI. He specializes in designing and delivering large-scale enterprise solutions across cloud, data, and AI ecosystems.
Over the past several years, Karan has led multiple enterprise AI initiatives involving Knowledge Graphs, Retrieval-Augmented Generation (RAG), Agentic AI, Context Engineering, and intelligent enterprise platforms. He has worked extensively with organizations across healthcare, life sciences, manufacturing, and technology sectors to help them transform enterprise knowledge into actionable intelligence.