arXiv:2606.28370v1 Announce Type: cross Abstract: Enterprise business intelligence queries span structured warehouses and unstructured document repositories -- modalities with fundamentally different access methods, cost profiles, and correctness semantics. Existing AI-enabled interfaces force users to select the right tool: NL2SQL systems cannot reason over slide decks, and RAG pipelines lack access to live warehouse tables. We present COGNI, a production conversational BI system that treats natural-language analytics as a heterogeneous query processing problem, organized as four architectura
The development of COGNI reflects the increasing maturity and integration efforts of AI technologies to address complex enterprise data challenges, bridging structured and unstructured data silos.
This system directly addresses a critical pain point in enterprise business intelligence by unifying access to diverse data modalities, enabling more comprehensive and natural language-driven analytics.
Traditional silos between structured warehouse data and unstructured document repositories are being bridged by a single conversational AI interface, transforming how businesses extract intelligence.
- · Enterprise AI providers
- · Large enterprises with diverse data assets
- · Business intelligence software vendors
- · Data scientists and analysts
- · Fragmented BI tool vendors
- · Companies relying solely on manual data integration
Increased efficiency in large-scale enterprise data querying and analysis through conversational interfaces.
Reduced need for specialized data engineering skills for basic BI tasks, democratizing data access.
Enhanced speed and accuracy of strategic decision-making due to holistic and rapid data insights, leading to competitive advantages.
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Read at arXiv cs.AI