Your data is already fast. Answering a new question still means waiting on a dashboard nobody built yet, because the fresh data sits in systems that were never meant to be joined. Adding an AI agent looks like the fix, but it moves the cost somewhere you can see it: the model tries to join data across systems, which is what models are worst at, so you pay in tokens and still get a partial, sometimes wrong answer.
The alternative is to stop asking the model to do the database's job. SingleStore unifies operational data, analytics, and vectors on a single copy, so the database handles the joins, filters, and similarity search. You ask in plain English through Aura Analyst, SingleStore's conversational analytics layer, which writes the query, runs it against live data, and hands the model a short, correct result to read instead of raw rows. The answer gets better and the token bill drops.
• Rapid Querying: Operational events streaming from Confluent (now part of IBM), queryable within ~300ms of landing.
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• Precision: A question no one pre-built, answered in plain English, with the generated SQL on screen so you can check the work.
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• Hybrid Efficiency: An exact filter and a semantic match in a single query, for example "find the shipments most like the one that just failed."
• Real-Time Adaptation: A live event injected mid-demo, and the answer changing on the spot.
• Cost Comparison: The same question side by side, three systems and a large token bill against one system and a fraction of the tokens.
Anyone who needs conversational analytics on real-time streaming data. The demo runs on a logistics scenario with multi-model data streaming from Confluent, but nothing about the pattern is industry-specific, so it lands the same way in retail, energy, financial services, or transportation. Data and platform leaders, architects, and AI teams watching token costs climb will get the most out of it.
