How Conductor Builds Sub Second Agentic Analytics at Scale

🎯 Whether you're rethinking your data infrastructure or exploring what it takes to make analytics agent-ready, this session from Dominik Lange and Uday Rajanna at Conductor is worth your time.🏗️ Dominik walks through the real story behind Conductor's data architecture overhaul — decentralized pipelines, cross-team bottlenecks, no single source of truth — and the full rebuild that got them to sub-second query performance on large datasets with CelerData Cloud. Uday builds on that, showing how the same infrastructure powers their MCP server and OpenAI/ChatGPT integration, and the split reasoning architecture that keeps agentic analytics accurate at scale — where the LLM handles intent, and the data API handles the precision work. 🎁 And don't miss the live demo of the Claude integration at the end — an autonomous agent running a full AI search audit in real time. ⚡ No shortcuts, no hand-waving — just the full picture. -------------------------------------------------------------------------------------------------------------------- Timestamps 00:00 Intro 01:41 Agenda 02:43 Conductor: Company Overview 04:55 The Challenges: Limitations of the Original Architecture 05:56 Conductor's Journey to Revamp the Data Infrastructure: Key Objectives 07:04 New Data Architecture 　　　07:55 Data Lake: Use Cases and Challenges 11:03 Solution: High-Performance Consumption Layer with CelerData Cloud 　　　12:28 Why CelerData 14:13 Results and Business Impact: Product Innovation, Always-Fresh Data, Democratized Data Access, and Faster Time to Market 　　　17:35 Use Case Showcase: Keywords and AI Search 19:49 CelerData Dashboard: Sub-Second to Second Performance on Large Data Scans 20:35 Agentic Analytics with MCP: The Use Cases 22:29 The Common Challenges of Agentic Analytics with MCP 24:08 Solution: The Split Reasoning Architecture for Agentic Analytics 26:11 CelerData Cloud: Key to Synchronous Response Architecture for the Data API 27:45 Live Demo: Conductor MCP Server + OpenAI/ChatGPT Integration 　　　29:39 Architecture Walkthrough: How Split Reasoning Works 　　　34:19 Advanced Use Case: Dynamic Sentiment Analysis 39:05 Q&A 　　　39:21 How do we handle cases where the API doesn't support what the user is asking? 　　　42:04 Can you try a question in the demo that's completely unrelated to the platform? 　　　43:30 Given how reliable this architecture is, do you see conversational AI agents fully replacing traditional dashboards — and what's your vision for where this product goes? 　　　48:16 Can you just ask AI to create a dashboard using natural language? 　　　49:45 Is this LLM-driven approach capable of producing high-quality charts and visuals that can be natively embedded into the Conductor app? 　　　52:39 If you had a magic wand to solve one problem in agentic architecture right now, what would it be? 57:06 Bonus: Live Demo with Claude --------------------------------------------------------------------------------------------------------------------- Learn more at https://celerdata.com/ Connect with us: LinkedIn: https://www.linkedin.com/company/celerdata/ Twitter: https://twitter.com/celerdata CelerData Website: https://celerdata.com/ StarRocks GitHub: https://github.com/StarRocks/StarRocks StarRocks Website: https://www.starrocks.io/ Slack: https://starrocks.io/redirecting-to-slack #AgenticAnalytics #DataArchitecture #DataEngineering #AIAnalytics #AgenticAnalytics #LLM #OpenAI #ChatGPT #Claude #AIAgents #AnalyticsEngineering #DataInfrastructure #AISearch #ConversationalAI #DataStack #MCPServer #DataAnalytics #RealTimeAnalytics #RealTimeData #OLAP #DataAnalyst #DataEngineer #DataInfrastructure #databaseprogramming