Context you can prove, from mainframe to cloud
The evidence-backed context layer for enterprise AI —
built from how your data is actually made, with every fact traceable to its source.
Licensed by a Nordic bank for business lineage and mainframe understanding
Enriched tens of thousands of assets with automated metadata at a Nordic telecom
Why agents get it wrong
Agents rarely fail for lack of intelligence. They fail for lack of context.
An agent that writes flawless SQL will still report the wrong revenue if nobody told it which revenue the business means. Models are converging; the context they reason with is what makes the answer yours — and what makes it right.
57%
of enterprises have traced a confident but wrong agent answer to missing or inconsistent business context.
Confidently wrong
The answer reads as correct, and nothing in it says otherwise.
Unprovable
Nobody can show where the figure came from, so nobody can stand behind it.
Out of date
Context that was right last quarter, still served as true today.
The evidence-backed context layer
Everything an agent needs to reason about your data — and everything a person needs to check it.
Where
Lineage across the whole estate
Business, technical and system-level lineage at column level, from mainframe to cloud — the path a value actually takes.
How lineage worksWhat
Meaning and business concepts
What data, transformations and business concepts mean, derived from how data is made: code, configuration, copybooks, documents and logs.
How meaning worksWhy
Evidence on every fact
Each fact cites the file and line it came from, knows how current it is, and names its gaps. When sources disagree, you see the disagreement instead of a guess.
How evidence worksBuilt once, from the source. Served to every agent.
One context for every consumer, so your agents and your people stop working from different versions of the truth.
AI agents
Analytics, coding, modernisation and compliance agents query the same context over MCP and A2A, with the evidence attached.
People
Explore lineage, read the business view, and open the evidence behind any fact.
Auditors
Follow any figure back to the code that made it — whether a person or an agent produced it.
Where context changes the answer
Four situations where evidence-backed context makes the difference — starting with the one that earned us a bank licence.
Context for modernisation
Lineage and meaning from mainframe code, so modernisation agents and engineers work from the same verified map — before, during and after migration.
Read moreContext for compliance
Evidence-backed lineage for BCBS 239, DORA, GDPR and the EU AI Act — for figures produced by people and by agents alike.
Read moreContext for engineering agents
Coding and data-engineering agents see what a column means, what depends on it and what a change will break — across legacy and modern systems.
Read moreData stewardship
AI-assisted descriptions, tags and cross-platform lineage, so stewards review the context agents rely on instead of authoring it by hand.
Read more
What people say
In production use at Nordic enterprises in banking and telecom. Customer names are withheld by agreement; roles and industries are accurate.
“Auto generated descriptions for the data assets are so rich, better than human-written ones.”
“The business lineage view is incredible, makes so easy to understand complex transformations.”
“No manual efforts anymore and our users are happy with the automated rich metadata.”
“PII classification and linking assets with terms is now a breeze, we feel much more confident in our compliance efforts.”
Common questions
What is a context layer?
A context layer sits between your data and the AI agents that use it, and turns raw data into meaning an agent can act on: what a figure means, where it comes from, and whether it can be trusted. Semantic Scout’s is evidence-backed — it builds that context from how your data is actually made, from mainframe to cloud, and every fact cites the file and line behind it.
How is this different from a data catalog?
A catalog learns what data means from how it is used: query history, dashboards, documentation. Scout derives it from what produces the number — the code and what ships with it — which is why the trail does not stop where the warehouse stops. We are not a catalog replacement; we sync to the one you run.
Do you use our business data to generate metadata?
No. No real business data is used in the process. Semantic Scout works from configuration, logs, and code — this is an architectural property, not a policy promise.
How fast can we see results?
Quickly, and without a long integration project first. Point Scout at a representative repository or pipeline and end-to-end lineage starts appearing from the first run, then deepens as coverage widens. Pilots reach broad coverage well inside the pilot window.
See your own context, built from your own code
A 30-minute demo shows the context layer. A pilot builds yours, from your own repositories.