How to get your Reltio data ready for agentic AI

Reltio holds the master, deduplicated version of your business's most important records — customers, products, and locations — along with how those records relate to and interact with each other, and the full history of how duplicates were matched and merged into a single trusted profile. Getting Reltio data ready for agentic AI means centralizing your entities, relationships, and match history in a warehouse or data lake where AI agents can query the full governance trail, join it with other data sources, and surface answers on demand. For a data governance or master data management leader, that turns Reltio from a system of record into a data foundation for agentic AI that explains not just what the trusted record says, but how it got that way. Fivetran + dbt Labs delivers the complete data foundation agents need — Fivetran moves Reltio data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Reltio data is critical for agentic AI

Governance leaders are accountable for data quality, but master data usually stays isolated in Reltio itself, separate from the analytics and reporting the rest of the business relies on. A question like how many duplicate customer records were merged this quarter, and whether the right record survived each merge, takes a manual audit today rather than a quick query. Relationships between entities — which accounts roll up to which parent company, which interactions tie back to which profile — are difficult to trace across a full portfolio without pulling everything into one place first. By the time a bad merge decision surfaces in a report, the downstream damage to customer or product records has often already spread. Master data infrastructure built for agents, not just analytics, keeps that governance trail queryable the moment a question comes up.

What agentic AI can do with Reltio data

A master data management leader can ask which entities were merged incorrectly and need review, and get an immediate list instead of running a manual audit across the entity graph. A data governance team can trace relationships and interactions across the full portfolio to answer who is connected to a given account in seconds, rather than following links by hand. A compliance team can verify that survivorship rules were applied consistently across every merge, not just a sample pulled for a periodic review. A business team can get one trusted view of a customer or product record, joined with other systems, instead of manually reconciling several sources to figure out which one is current.

Each of these depends on having entity, relationship, interaction, and match history available together, not siloed inside Reltio's own interface. Once that history exists in one place, an agent answers governance and lineage questions on demand, using the same match and merge records Reltio already generates.

How Fivetran gets your Reltio data ready for agentic AI

Master data lives across entity, relationship, interaction, and match records that update continuously and reference one another, which makes it hard to see a complete governance history outside Reltio itself. Fivetran incrementally syncs entity, interaction, match, and relation records, along with their supporting type and reference data, into your warehouse or data lake, keeping merges, unmerges, and match history intact rather than flattening them into a single current-state snapshot. It preserves merge and unmerge events specifically, tracking which entity became the surviving record and marking superseded ones rather than deleting them, so the audit trail your governance team depends on stays complete. From there, dbt Labs transforms and governs the raw entity graph into clean, trusted, AI-ready tables — applying the same modeling, testing, and documentation rigor to the data pipeline that governance teams already expect from Reltio itself.

What your Reltio data unlocks for your team

With Reltio data in a central warehouse, AI agents can unlock capabilities your team couldn't access before.

• Unified master record view — agents can query the current trusted profile for any customer or product on demand.
• Full merge and match audit trail — every merge and unmerge decision stays traceable, not just the current outcome.
• Entity relationship mapping — connections between accounts, products, and interactions become queryable across the whole portfolio.
• Data quality monitoring — governance teams can spot inconsistent survivorship or match patterns before they spread.
• Governed cross-system joins — trusted master data joins cleanly with other business systems without duplicating governance work.

FAQ

What does it mean for Reltio data to be AI agent-ready?

It means your entity, relationship, interaction, and match data is centralized, cleansed, and governed in a warehouse or data lake, so an AI agent can query the full master data and merge history instead of only the current state inside Reltio.

What can my team actually do with AI agents and Reltio data?

Teams can ask direct questions about merge history, entity relationships, and data quality and get immediate answers, instead of running manual audits across the entity graph each time a question comes up.

Is Reltio data ready for AI agents out of the box?

Not without preparation. Reltio data needs to be centralized, modeled, and governed before an agent can query it reliably.

Do we need a data engineering team to set this up?

No. Fivetran automates the sync of entity, relationship, and match data into your warehouse or data lake, so your governance team does not need to build a pipeline from scratch.

How does Fivetran get Reltio data ready for AI agents?

Fivetran incrementally syncs entity, interaction, match, and relation data reliably into your warehouse or data lake, preserving merge and unmerge history rather than overwriting it. dbt Labs then transforms and governs that data into clean, AI-ready tables using its full modeling and testing capabilities.

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