> 20 August, 2026: 9am PST / 6pm CEST
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> See conversational analytics tackle unscripted questions on live enterprise streaming data in a live, head-to-head demonstration.
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> View the underlying SQL for every response and learn how to achieve this at a fraction of the token cost compared to a traditional three-system stack.
Production environments expose the flaws of AI pilots that look great in isolation. When applications scale, answers must remain accurate, verifiable, and cost-effective under regulatory scrutiny. However, connecting an LLM to disparate data sources forces the model to perform complex data joins—a task it poorly executes. This results in lagging response times, unverified outputs, and skyrocketing token expenses.
The solution lies in offloading database functions from the LLM. By executing filters, joins, and vector searches within a unified engine, transactions, analytics, and vectors are maintained on a single live data copy. The model can then process concise, accurate outputs rather than raw datasets. This approach powers SingleStore Aura Intelligence and its conversational layer, Analyst. This session features a head-to-head comparison between SingleStore and a traditional three-system architecture, demonstrating the differences in precision and token efficiency.
Mohamed Kheir, SingleStore’s Field CTO for International, will conduct an unscripted, live demonstration utilizing real-time streaming data from Confluent to answer spontaneous questions.
• Instant answers to unplanned, natural-language queries executed against live streaming data.
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• Full transparency with live SQL generation displayed on screen to verify all results.
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• Unified queries combining precise filtering with semantic matching, such as locating shipments matching a failed delivery profile.
• Dynamic updates as live data events are injected directly into the active demo.
• A side-by-side performance and token-cost breakdown between a single-engine solution and a three-system setup.
This session is designed for data and platform leaders, enterprise architects, and AI teams looking to implement conversational analytics on real-time streaming data while managing escalating token expenses. While the demo highlights a logistics use case, the core architecture applies to financial services, retail, energy, manufacturing, transportation, and other industries reliant on real-time data.
