Executive Summary

Enterprises that deploy AI assistants and automated BI increasingly rely on generated SQL to answer business questions, yet most organizations lack reliable provenance tying a natural‑language response or dashboard cell back to the exact SQL, schema versions, and upstream tables; this problem affects analytics teams, data engineers, compliance officers and roughly 160,000 data‑using organizations that could be targeted. The operational consequences are concrete: harder incident resolution, slower audits, and eroded trust in assistant outputs and BI reports, with material cost for large customers who could pay $60K ACV for enterprise lineage and assistant integrations. You could build a runtime and historical provenance platform that captures SQL at the point of generation and execution, fingerprints queries, maps them into a unified lineage graph, and surfaces explainability views tying assistant answers and BI visuals back to the underlying SQL, datasets, and schema versions. Key capabilities would include lightweight SDKs for AI assistants, connectors for Snowflake/BigQuery/Redshift and Looker/Tableau/Power BI, a provenance UI for auditors, and alerting/analytics for data ops; pricing targeted at ~$60K ACV aligns with the $9.6B market estimate and enterprise buyer willingness. The market is attractive now because AI explainability, data observability, and analytics automation are converging—enterprises are spending to avoid data incidents and to explain model outputs, which is reflected in a market score of 92/100 and revenue potential of 84/100. To stand out you must deliver depth across the assistant–BI boundary (runtime tracing plus historical lineage), low overhead instrumentation, and enterprise security/compliance features, while acknowledging real challenges: integration fragmentation across tools, potential performance impact, and a medium competitive landscape of existing lineage and observability vendors that will require clear product differentiation and strong go‑to‑market execution.

Market Opportunity

Track SQL data provenance inside AI assistants and BI reports targets a $9.6B = 160,000 data-using organizations x $60K ACV (analytics lineage + assistant integrations) total addressable market with medium saturation and a year-over-year growth rate of 18% — reflects growth in BI/observability and AI augmentation adoption.

Key trends driving demand: AI explainability -- enterprises demand tools that can explain model outputs and the underlying data used to generate answers.; Data observability -- more teams invest in lineage and monitoring to reduce downtime and data incidents.; Analytics automation -- growth of AI-assisted BI increases reliance on automated query generation that needs traceability.; Warehouse-first tooling -- Snowflake/BigQuery/Redshift expose APIs making integrated provenance feasible with low friction..

Key competitors include Monte Carlo, Collibra, dbt Labs, Datafold, Warehouse-native logs (Snowflake/BigQuery query history).

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Track SQL data provenance inside AI assistants and BI reports

Executive Summary

Market Validation

Market Opportunity

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