Prove the loaded estate
Full-window proofs across the migrated tables, with the differences isolated to rows and fields the migration team can act on.
Solutions
Four places a copy has to be right.
The same proof, the same evidence, applied to the datasets consumers read, the systems agents act on, the controls auditors test and the cutovers nobody wants to repeat.
Data consumer confidence
Give every consumer confidence in the target data they use.
Validate that copied data matches its source before consumers use it in dashboards, data products, workflows, applications, AI agents or MCP tools.
What goes wrong
- Consumers challenge numbers and nobody can say which side is right.
- Data products launch without acceptance evidence.
- Target systems drift from their sources between checks.
- Manual SQL checks slow every investigation down.
- Teams cannot tell load timing from a true mismatch.
What Paridity gives you
A repeatable way to prove that the target copy matches the source, in the window that matters, and to explain the discrepancy when it does not: which rows, which fields, which side.
AI agent and MCP confidence
Prove the data your agents and MCP tools depend on.
Paridity validates source-to-target correctness for datasets and systems exposed to agents, copilots, MCP tools, retrieval workflows and automated actions. An agent can only be as correct as the target data it reads.
- Which target data an agent depends on
- And which source it should match, declared as a mapping, not inferred.
- Which window was compared
- So an answer from five minutes ago is not judged against a load that finished after it.
- Whether proof passed
- And, when it did not, which rows and fields differed.
- Which config and policy were active
- And what evidence exists for review, exportable to whoever has to sign off on the agent.
Compliance and controls
Turn copied-data checks into repeatable controls.
Paridity helps finance, data and audit teams document whether critical copied datasets were complete and correct at the time of comparison, with a control that is independent of the system that moved the data.
- What mapping ran, and which configuration version was active, by checksum.
- Which window was compared, and when the comparison started: a timestamp that never changes.
- Whether the proof passed, and which rows and fields differed when it did not.
- Who triggered or reviewed the run, with network attribution.
- When the run reached its terminal status, logged with the actor.
- What was purged under retention, and when, with counts.
- Signed evidence bundles per period, with a hash chain across periods.
- A run that could not verify its schema, or ran with reduced coverage, labeled as such and never counted as clean.
Migration validation
Validate source-to-target parity before and after cutover.
Use Paridity during a migration to compare legacy source datasets with the new target system, then keep the same confidence workflow running after go-live. The same mappings, the same evidence, the same people reading it.
Evidence for the go or no-go
A pass per mapping, with the window and the configuration recorded, for the people who sign the cutover.
The same checks, scheduled
Incremental windows on the replication that follows, with CDC-aware readiness so a replication lag never reads as a migration defect.
Which of these is yours?
Tell us the source, the target and the replication tool, and we will show you the proof on a table you choose.