Tamr, an AI-native master data management (MDM) solution, is launching Curator Hub, a new module that helps organizations tackle quality issues in their data—and prepare it for successful generative AI use—by pairing intelligent agents with human expertise.
According to the company, as data quality becomes the biggest barrier to trustworthy AI, Curator Hub offers a smarter, more scalable way to resolve inconsistencies, fill gaps and connect related records.
Teams can work faster; reduce manual effort; and make better, data-driven decisions, while seeing greater returns on AI initiatives.
Tamr’s machine learning automates the bulk of the often-onerous data mastering process: unifying, cleaning, and enriching enterprise data at scale.
Curator Hub marks Tamr’s entry into agentic data curation—the use of LLM-based AI agents that can reason through complex data issues, suggest updates, and explain their logic in plain language.
Designed as the “mission control” for human-AI data curation, Curator Hub gives data stewards a central space to manage issues flagged by AI agents or submitted by users, the company said.
Through a combination of data quality standards, real-time duplicate checks and background agents, edge cases are automatically flagged in a curated queue, where stewards can see side-by-side comparisons; understand why an issue was flagged; and preview what will change before applying an update
Curator Hub will be available as part of Tamr’s AI-native MDM platform to all Tamr Cloud customers. It includes a range of features to help teams scale data quality, all within a customizable, user-friendly workspace:
Prioritized issue queue: Focus on the most pressing problems first. Curator Hub surfaces potential duplicates, missing values and anomalies, ranked by urgency by Tamr’s agents.
Decision-ready views: See side-by-side comparisons, understand why issues were flagged (with labels and confidence scores), and preview changes before applying updates.
Golden record refinement: Improve accuracy by moving or reassigning source records to ensure the correct grouping and composition of mastered entities, such as a person or organization.
Customizable workflows: Define how issues are routed, when agents are triggered, and which cases require human review.
Transparent curation history: Track who made what changes and when, helping teams audit decisions and maintain data governance.
System health insights: See resolution progress, monitor issue volume, and visualize data quality trends in real time.
Built-in (and expanding) agent library: Tap into a starter set of prebuilt AI agents, with more on the way, including industry-specific tools, and reusable logic templates.
Bring Your Own Agent (BYOA): Integrate your custom-built agents into Tamr’s workflow using a supported low-code approach.
“Data projects lose momentum and value when results take too long to materialize,” said Anthony Deighton, CEO of Tamr. “Curator Hub changes that, giving stewards an intuitive way to see, make and explain data improvements. It also provides an important vehicle for leveraging AI agents to automate and improve data curation efficiency—representing a pivotal shift in how organizations can build trust in their data and realize greater returns from downstream AI applications.”
Curator Hub is part of Tamr’s broader mission to bring clarity to enterprise data by combining machine intelligence with human expertise. By helping teams review, approve, and trust the work of AI agents, Tamr is redefining how organizations scale and govern their most valuable data assets, the company said.
For more information about this news, visit www.tamr.com.