Validation
Does it obey the business?
Encode the conditions only your team knows — every order resolves to a customer, refunds never exceed the sale — and enforce them on every run.
- predicates
- lookups
- policies
The Unified Data Trust PlatformOne layer of trust
One layer of trust
around all your data.
Yugma brings validation, reconciliation and data quality together — then adds a GenAI intelligence layer that writes the rules, spots the anomalies and finds the root cause.
Your raw business data stays inside your environment. The AI works on metadata.
GenAI intelligence layerMetadata only
creates rulesrecommends checksdetects anomaliesroot-cause analysis
Validationbusiness rules
Reconciliationsystem vs system
Data qualityhealth over time
Your warehouseraw business data never leaves
Three capabilities, one platform
Trust is not one check. It is three.
Most teams run these in three different tools, with three different answers. Yugma runs them on one engine, against one catalog, with one audit trail.
Reconciliation
Do the systems agree?
Prove a copy still matches its source across warehouses — counts, schema, types, statistics, content hashes, and the exact rows that differ.
- 7 checks
- row-level diffs
- cross-platform
Data quality
Is it healthy over time?
Measure continuously against thresholds you set — completeness, uniqueness, validity, freshness, distribution and referential integrity.
- 6 dimensions
- scheduled
- trended
The difference
An intelligence layer on top, not a chatbot bolted on.
The same layer that understands your rules and your measurements can also write them, question them, and explain them — because it sees the whole trust surface, not one tool's corner of it.
Creates rules
Describe the requirement in plain English; Yugma drafts it against your real schema.
Recommends checks
Suggests the quality dimensions a table actually needs, based on how it profiles.
Detects anomalies
Reads historical measurements and flags the shift that thresholds alone would miss.
Accelerates root cause
Follows lineage and run history to say where it broke, not just that it broke.
Privacy-conscious by design
The AI never needs your business data.
It reasons about the shape of your data and how it behaved — not the rows themselves. That boundary is the architecture, not a setting.
Works on
- Metadata — tables, columns, types, keys
- Data quality metrics and their thresholds
- Rule results — what passed, what failed
- Historical measurements and lineage
Stays with you
- Raw business records — customers, orders, amounts
- Personal and regulated fields, in any form
- Warehouse credentials — sealed before storage
- Comparisons run in your warehouse; only verdicts return
See it run against your own tables.
Thirty minutes. Connect one warehouse, pair two tables, and watch what it finds.