Demonstration of Pneuma-Seeker: Agentic System for Reifying and Fulfilling Information Needs on Tabular Data
For data analysts, this system addresses the bottleneck of refining underspecified queries on relational data, but the contribution is incremental as it applies existing LLM capabilities to a known workflow.
Pneuma-Seeker reifies vague information needs into explicit relational specifications for tabular data, enabling iterative refinement and provenance-aware execution. Demonstrated on two procurement use cases, it shows LLMs as transparent analytical collaborators.
Data analysts working with relational data often start with vague or underspecified questions and refine them iteratively as they explore the data. To support this iterative process, we demonstrate Pneuma-Seeker, a system that reifies a user's information need as explicit, inspectable relational specifications, enabling iterative refinement of the information need, targeted data discovery, and provenance-aware execution. Through two real-world procurement use cases, we show how Pneuma-Seeker leverages LLMs as transparent, interactive analytical collaborators rather than opaque answer engines.