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AGENTIC DATA & CONTEXT QUALITY

Get your data trusted & AI-ready

In weeks, not years.

Build a layer that unifies business meaning across systems and exposes quality gaps in your key metrics.

See how it works
FIELDcustomer_idDQ RULEemail must be uniqueFabricFIELDKNA1.KUNNRBUSINESS RULEcredit block above €50kSAPFIELDarr_eurDQ RULEno null countrySnowflakeBUSINESS RULEchurn risk > 0.7 = at riskDatabricksDEFINITIONActive = invoiced in last 12 monthsCONTEXTEU customers under GDPRSharePointPROCESSOnboard → Active → ChurnedDEFINITIONKey account = ARR > €250kConfluenceCLARITYONE GOVERNED MODELdefinitions · rules · fields · processes · contextDefinitionSAPSalesforceSharePointProcessRuleCONCEPTCustomer4 SOURCES · 1 OWNEROUT OF SYNCSnowflakeSAPFabricOpenAIClaudeCopilotCONTEXT & QUALITY ENGINEYour teamasks it questionsYour sourcesget the fixesAI agentsuse it as context

Scan for hidden definitions and implicit rules across data and documents.

See the platform What goes beyond your metrics
Context engineering

Trusted data needs both AI and human judgment.

AI can't build its own scaffolding yet. It can't pick between two competing definitions of net revenue, but it can find every version in your organization and help you choose.

What is context engineering?How to build a use case for this
Patrick van Kalken, Chief Data Officer
Patrick van Kalken
Chief Data Officer
“Manually, this would be a multi-year program with nothing to show for the first year. With Metagem we prioritize a handful of quality conflicts that actually matter, and fix those with the business.”
€7.5B manufacturing groupNutreco

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