Abstract

A chatbot can answer general questions. It can't tell you why a key metric dipped last week, because it can't see your data, tickets, documents, or code. This talk shows how we turned a generic chat assistant into a "company brain": one place where employees ask questions in plain English and get answers drawn from the systems they already use. We start with a chat interface that can use any large language model. We then add three building blocks:

• Connectors to internal data, documentation, ticketing, and communication systems, built on an open standard (the Model Context Protocol).
• Skills, which are centrally maintained, reusable bundles of domain knowledge.
• Tools such as web search and code execution. Wiring these up by hand in every chat doesn't scale, so we package them into pre-built agents. Specialist agents each own one domain, and an orchestrating agent routes every question to the right specialist. Connectors run under each user's own identity and are read-only, so the assistant only sees what the person asking is allowed to see. You'll leave with a practical blueprint: how to layer models, connectors, skills, and agents, how to handle permissions, and what to watch out for when rolling it out company-wide.

Bio

Shekhar Khandelwal is a Principal AI Engineer at adjoe, part of the AppLike Group. He has more than a decade of experience building enterprise-grade AI products, including roles at Accenture, IBM, and EY. He holds a master's degree in Data Science and is the author of two books on deep learning. At adjoe, he builds AI agents, agentic workflows, and AI solutions.