The cognitive infrastructure between your questions and your data. Not a query tool. Not a BI layer. Something entirely new.
No SQL. No schema knowledge. No documentation needed. Ask the way you think. VertiscopeAI handles the rest.
Multi-stage semantic reasoning extracts entities, filters, relationships, and aggregations — mapping your question to exactly the right data across all connected sources.
Each database is queried in its own native dialect simultaneously. Oracle gets Oracle SQL. Postgres gets Postgres SQL. Your data never moves. Zero translation errors.
Results are reconciled centrally, deduplicated, and returned with full source provenance. Every row knows where it came from.
VertiscopeAI doesn't just find tables — it understands relationships, resolves ambiguity, and plans joins that would take a data engineer hours to write.
type=aggregation · entities=[revenue, region, store_count]
confidence 0.96revenue → Oracle ERP · region → SQL Server retail · store_count → SQL Server metrics
2 sources selectedregion relationship resolved across sources · queries dispatched in parallel
ready to execute12 rows · 2 sources · 643ms · audit logged
✓ successLLM-powered SQL generation with schema-aware context, self-correction, and dialect-specific output for each connected source.
Automatically splits multi-database queries into per-source sub-queries, executes in parallel, and merges results with intelligent joins.
Multi-stage semantic reasoning extracts filters, aggregations, and relationship hints from natural language — with high precision across diverse query styles.
Every finalized query is stored as a learned pattern. Repeat queries skip the LLM entirely and execute directly — instant recall, zero overhead.
Every relationship between tables is surfaced. See exactly which columns joined which sources, and why — full transparency, no black boxes.
Generated SQL is validated against live schema before execution. Errors are auto-corrected in real time — not in a ticket queue.
| Capability | Vertiscope AI | Traditional BI | Data Warehouse |
|---|---|---|---|
| Cross-database federated query | ✓ Native | — ETL required | — Centralise first |
| Natural language interface | ✓ | Limited / bolt-on | — |
| Zero data movement | ✓ Query at source | — | Ingest required |
| Sub-second federation | ✓ Parallel execution | — | Pipeline lag |
| On-premise deployment | ✓ | Sometimes | Sometimes |
| Learned query patterns | ✓ AI learning loop | — | — |
Enterprise pilots available now. Deploys in your infrastructure.
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