WORKSHOP
Agentic Analytics in Regulated Industries: Live on Exasol (WS09)
Date: 08 October 2026 | Time: 11:30-02:00 PM
Venue: Workshop Room 3, 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)
Show what it takes to put an AI agent on top of enterprise data in industries where every answer may be audited. Using two live builds on public data — a clinical trial landscape assistant for pharma and a near-real-time fraud-scoring pipeline for banking — we demonstrate an agent that discovers schema over MCP, applies the organisation’s definitions through a semantic layer, runs inference inside the database, and leaves a query log that can be replayed months later. The aim is that attendees leave knowing the difference between an agent that has been handed database access and a database built for agents.
Prathamesh Karmalkar
Staff Software Engineer, Exasol AG
- Anyone building or evaluating AI agents over real data — data engineers, backend developers, analytics engineers, architects, and data scientists moving from notebooks to production. No prior experience with Exasol, MCP or the pharma and banking domains is needed; both use cases are explained in plain terms before each demo. It also suits engineering leads and product owners who need to understand what "agentic analytics" actually requires before committing to a platform. Students and early-career developers with basic SQL and Python are welcome.
- Why agentic analytics — what changes when an agent, not a dashboard, answers the question
- What an agent needs from its database: schema discovery, semantic definitions, identity to the row filter, inference inside the boundary, a replayable log
- Exasol in brief — the engine, the open-source MCP server, agent skills, Personal edition
- Demo 1 (pharma): clinical trial landscape assistant over ClinicalTrials.gov — load, semantic view, MCP agent, cited answers, AI functions over eligibility text, audit log
- Demo 2 (banking): near-real-time fraud scoring — Kafka ingestion, in-database scoring UDF, the same agent querying the live stream, row-level policy applied live
- Where this approach doesn't save you
- Take-home kit and Q&A
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)
A working mental model of agentic analytics that goes beyond “connect an LLM to a database.” Attendees see the five properties that separate a production-grade setup from a demo, and watch each one enforced live rather than described. They leave with a public GitHub repository containing both builds, the public datasets as snapshots, and a one-command local install of Exasol Personal with the MCP server pre-wired, so everything shown can be reproduced at home. They also get an honest view of the limits — what in-database agents cannot do, and where other tools remain the right choice.
Bring a laptop — this session is built to be followed along. Setup instructions will be shared a week before the event; please complete them in advance, as we won’t troubleshoot installs in the room. The core step is installing Exasol Personal Local via the Exasol Starter Kit, a one-command setup that provisions the database, the MCP server and the Python tooling together. It runs on macOS (Apple Silicon, 8 GB RAM), Linux (Podman installed), and Windows 10+ x64 (Podman; admin rights may be needed). Basic SQL and Python helps. A GitHub account and an MCP client such as Claude Desktop or Cursor are needed to run the agent yourself.
About Speakers
Prathamesh Karmalkar is a Staff Software Engineer at Exasol AG with over 12 years of experience in AI, Generative AI, NLP, LLMs, machine learning, and deep learning. Recognised among NASSCOM Makers Honor 2026’s Top 30 Engineers of India and as one of the Top 7 AI Scientists at DataHack Summit 2026, he has published 10+ papers in peer-reviewed journals and presented at international conferences. As an AI Product Owner, he focused on building LLM-powered solutions that enable informed decision-making using secure, enterprise data.