The Only AI-Native Master Data Management Solution

Tamr is the clear, differentiated choice for companies ready for a modern approach to data mastering—one that operates faster and at lower cost, achieves far greater scalability, and produces better, more accurate results than any alternative.

A world where everyone has the best data, everywhere, all the time

MIT-Born, Mission-Focused

Founded in 2013, Tamr was built upon the groundbreaking work of Turing award winner Dr. Michael Stonebraker and his research at MIT’s Computer Science and AI Lab. Since then, Tamr has maintained its mission of empowering companies to unify, clean, and improve their enterprise data—using a modern, purpose-built SaaS platform—and ensure it is high-quality, trusted, and accessible for all downstream applications.

Tamr’s AI-Native Advantage

Tamr takes a fundamentally different approach to master data management (MDM) compared to other vendors in the market. Instead of relying on outdated, rules-heavy methods, Tamr employs patented AI to handle the majority of the data mastering work. Everything we deliver—from architecture to workflows to user interfaces—is built around AI. Tamr pioneered this AI-native approach, which significantly outperforms legacy MDM solutions, providing faster access to clean and trusted data, less manual effort, and a stronger foundation for analytics, operations, and AI initiatives.

Tamr’s Modern Architecture

Using the powerful combination of machine learning (ML), agentic AI, and human feedback, Tamr unifies, cleans, and enriches data across sources to produce accurate, complete, continuously maintained golden records. Our modern, event-driven, API-first architecture is built for scale, allowing it to easily integrate with virtually any other platform and support agentic AI workflows and a unique “bring-your-own-agent” model.

Fit-for-Purpose AI/ML Models

Tamr employs different AI/ML models for each part of the data mastering and data curation process, ensuring use of the right tool for the right job.

ML models quickly and efficiently tackle the core tasks of entity resolution, match verification, data standardization and normalization, and schema mapping.
Deep learning models deliver exceptional performance and high-quality results for real-time semantic search.
GenAI models power agentic data curation to address difficult edge cases and minimize excess human involvement.

Distinct from Other MDMs

Tamr’s AI-native approach stands in stark contrast to rules-based MDM solutions such as Reltio, Informatica, Boomi, IBM Infosphere, and Semarchy, which rely on coding logic and manual processes to standardize and match data across systems. These solutions demand constant rule updates, centralized control, teams of people to maintain, and a significant effort to scale.

These vendors say they are “AI-powered,” but in reality they are AI-enhanced, bolting AI components on top of an existing MDM architecture rather than embedding them at the core of the solution. These AI features typically recommend rules, provide chatbot functionality, or assist in resolving one-off data issues—but they struggle to execute high-volume data mastering and introduce complexity and challenges related to performance, scalability, explainability, governance, and user experience.

Engineered for Performance and Scalability

In addition to Tamr’s core AI/ML capabilities, three foundational elements underpin all of our AI functionality: a secure, scalable, SaaS infrastructure; highly tuned data products that address a variety of use cases; and continuous model training that keeps getting better and better at solving the master data management challenges facing organizations today.

Applying Best Practices to Drive Results

Tamr has pioneered the MDM Journey, our differentiated approach to delivering trustworthy data in days or weeks, not months or years. By following best practices and a prescribed sequence, Tamr customers have achieved optimal results by assessing and improving their data, thoughtfully reviewing it with end-users, and ultimately operationalizing their data by connecting it to essential business systems and generative AI applications.

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