Turning raw schemas into metadata agents can trust.
Agent-Ready Databases is a B2B product for enterprises that need AI analysts and data agents to understand what tables mean, how they join, and where assumptions need human review.
The workflow profiles data deterministically, asks targeted questions, generates typed metadata, and keeps every inference reviewable.
expected on dominant column and table semantics stages
targeted interview prompts before opaque-schema runs
primary keys and relationships verified beyond samples
Documentation that downstream AI can actually consume.
Existing enterprise metadata is often incomplete, stale, and disconnected from real data. The product creates a typed semantic layer that tells AI agents which tables to select, how to join them, what one row represents, and what uncertainty needs review.
What the product generates
- Column-level business names, descriptions, usage notes, PII flags, and assumptions
- Table-level grain, archetype, primary key candidates, and usage pitfalls
- Schema-level relationships, join purposes, potential analyses, and review flags
- Refinement routing so expert edits rerun only the affected layers
Why buyers care
- Downstream data agents get trustworthy grain definitions, join semantics, and usage pitfalls before they write queries
- Data stewards review high-impact ambiguity instead of manually documenting every column from scratch
- The product can be onboarded with months of hands-on engineering integration for enterprise-specific schemas, docs, and governance
Built around verification before interpretation.
The product combines deterministic profiling, LLM interpretation, full-data checks, and steward feedback so metadata becomes both rich and accountable.
Deterministic profiling
Pandas and Snowflake SQL collect types, nulls, distinct counts, top values, and full-table verification signals before interpretation
LLM semantics
Batched async prompts generate typed column, table, and schema metadata with explicit ambiguity and impact labels
Verified relationships
GROUP BY and JOIN checks confirm keys and relationships on real data so agents do not infer joins from names alone
Steward refinement
Human feedback becomes structured deltas, cascades only where needed, and prevents the same correction from being overwritten later
Want your data ready for agents?
Phinest can bring the product, the integration work, and the AI engineering needed to make enterprise schemas reliable enough for autonomous analysis.