Use case
AI agents are only as trustworthy as their context
Machine-readable lineage and meaning that give agents — and the people reviewing them — governed context and one-click provenance.
The situation
Copilots and agents are arriving in your data estate — writing queries, answering business questions, taking actions. An agent without lineage and meaning confidently acts on data it misunderstands, and under the EU AI Act, "the model seemed sure" is not a defence.
What Semantic Scout does about it
A governed context layer
Lineage and meaning, machine-readable, so humans and agents work from the same truth instead of each inventing their own.
Provenance for every answer
When an agent or an analyst uses a number, the source-to-report path is one click away — which is what turns a plausible answer into a defensible one.
Proof of the approach
Our own chat over transformations is an agent grounded in harvested metadata, working today on the hardest estate there is: mainframe COBOL.
Proof
In production use at Nordic enterprises in banking and telecom.
“Auto generated descriptions for the data assets are so rich, better than human-written ones.”
Customer names are withheld by agreement. Roles and industries are accurate.
Other use cases
Mainframe understanding & migration
Lineage from mainframe assets, COBOL programs explained through chat, and migration impact analysis backed by evidence.
Read moreRegulatory compliance
Continuous, column-level lineage and PII classification that turn BCBS 239, DORA, GDPR, and EU AI Act evidence into a by-product of normal operation.
Read moreData stewardship
AI-assisted descriptions, tags, and cross-platform lineage, so stewards spend their time reviewing rather than authoring.
Read more
Give your agents something to stand on
See what governed context looks like against your own pipelines rather than a vendor sandbox.