AI Agents at Work: Transforming Data Management with Tamr

Among its myriad applications, artificial intelligence (AI) can rapidly transform how organizations assess, improve, and consume their data. At Tamr, we’re at the forefront of this transformation, incorporating agentic AI into our AI-native MDM solution to increase efficiency and improve the quality of your data. According to Forbes, agentic AI refers to “artificial intelligence systems that possess a degree of autonomy and can act on their own to achieve specific goals.”

Designed to harness the power of AI and machine learning, our AI-native master data management (MDM) platform automates data mastering processes, replacing traditional, manual methods with a scalable, intelligent solution that radically improves efficiency and results. We have found that with traditional MDM implementations, it is common to hire large teams of human curators whose job is to manually review and remedy data errors. It is not uncommon for companies to hire teams (typically in low-cost labor markets) to do this manual work. Not only is this expensive, but it’s error-prone and slow. Tamr employs AI agents that perform tasks autonomously, continuously, and at scale, working tirelessly on the company’s behalf to automate complex, repetitive tasks and unlock new possibilities for their enterprise data.

How Tamr Uses Agentic AI to Revolutionize Master Data Management

Here’s a closer look at how Tamr’s AI agents deliver value across four key areas.

1. Curating Data at Scale: AI Agents as Data Stewards and Curators

Imagine having a team of data stewards and curators specifically trained to handle repetitive, detail-oriented tasks such as enriching addresses, identifying nicknames like Matt for Matthew, or reconciling data discrepancies. Sounds expensive and tedious, right? Tamr’s AI is up for the task. Acting as a team of tireless data stewards and curators, our solution automatically performs the same tasks as humans would but with exponentially greater speed, accuracy, and scale and drastically lower cost. 

Unlike traditional methods that depend on expanding human resources, Tamr’s AI-first approach offers a scalable alternative—continuous scanning, enriching, and improving records across systems without increasing headcount. By automating data curation tasks, AI agents not only reduce operational costs, but also ensure data quality and consistency at a scale that would be impossible for human teams to achieve. 

2. Smart Record Matching 
Traditionally, record matching has relied on static processes like clustering algorithms, which are effective, but limited, in handling the complexities of modern data environments. Unlike static processes or one-time efforts, Tamr’s agentic AI continuously scans and evaluates records, ensuring your organization’s data is always up-to-date, reliable, and actionable.
Let’s imagine that your organization uses Salesforce as the primary CRM but also has customer data scattered across other internal systems like Oracle and Marketo. You may also have multiple Salesforce instances resulting from acquisitions or regional teams working independently.

Tamr’s platform can pick up a record from one Salesforce instance, such as a customer named "John Smith," and ask:

• Does it match a lead in Marketo?
• Is it the same as a contact in Oracle?
• Does it already exist in another instance of Salesforce?

With Tamr, this process is continuous and proactive, dynamically identifying potential matches in real time. When unsure how to resolve an entity, the AI surfaces possible connections and relationships, enabling data stewards to decide how data across systems should be linked.

‍3. Context-driven Interactions
Tamr leverages Retrieval-Augmented Generation (RAG) to redefine how users interact with their enterprise data. Through our Virtual Chief Data Officer (vCDO) interface, we enable users to ask sophisticated, context-driven questions and receive immediate, actionable answers—powered by the unified data foundation of our AI-native MDM platform. Tamr’s vCDO surfaces context (“RAG”) to the large language model (LLM) of your choice through a set of APIs that make it easy for the model to retrieve context for the question the user is asking. LLMs know a lot about language, but don’t know anything about your business (your customers, your suppliers, your products, etc.). This capability makes it much easier for these models to “know” your business.

For example, when a user asks “What do we know about ABC Corp?”, Tamr’s vCDO doesn’t just retrieve fragmented details. Instead, it pulls together a comprehensive, contextualized view, providing insights such as “What ABC Corp has purchased from us?”; “Do we have any active contracts with ABC Corp and, if so, what are the terms?”; and “When was the last interaction with ABC Corp?” 

The vCDO goes beyond simple data retrieval by understanding the intent behind the question. It allows users to ask complex questions about their customers, suppliers, or operations without sifting through multiple systems. Whether preparing for a customer meeting, analyzing supplier relationships, or reviewing contract performance, users can rely on our vCDO agent to provide accurate, context-aware answers that drive smarter decisions.

‍4. Preventing Data Degradation
A major challenge in data management is avoiding duplicates and maintaining data integrity when new records are added. Tamr’s agentic AI works in the background as a real-time data assistant, ensuring every new record added to systems is accurate, consistent, and connected to the broader data ecosystem.

Here’s how it works. When someone adds a new record (e.g., a customer or supplier) to Salesforce, for example, the AI checks existing databases to see if the record already exists. It quickly determines whether the entry is new or matches an existing record and provides a response in real time: “This is a new record. Go ahead and add it.” OR “This matches an existing record. Here’s the relevant data.” 
Once validated, the record is seamlessly integrated into the system, and any additional details—such as addresses or contact information—are enriched through Tamr’s data curation processes. This approach eliminates duplicate entries, ensures data consistency, and saves users from manually searching for or resolving potential overlaps.‍

Why Agentic AI Matters

Tamr’s innovative use of agentic AI is transforming data mastering by enabling:

• Automation at scale: AI agents perform tasks that previously required significant human effort – and they do so at an unprecedented scale and speed.
• Smarter contextual insights: By combining RAG with AI-native MDM capabilities, Tamr empowers organizations with actionable insights in real time.
• Greater efficiency: Processes like record matching and enrichment are no longer bottlenecks, freeing teams to focus on higher-value tasks.

At Tamr, we believe that AI isn’t just a tool—it’s a partner in transforming how organizations manage and interact with their data. From continuous data curation and intelligent record matching to real-time contextual insights with vCDO and smarter data entry processes, our AI-native MDM platform is designed to help organizations derive maximum value from their enterprise data.

That said, you can’t assume AI will get it right 100% of the time, so Tamr’s agentic AI identifies lower confidence situations and puts those data records into an “inbox” for curators to inspect and adjudicate. This raises the profile of the data curation work and limits it to those situations when humans are most needed.

Discover the powerful ways that AI-native MDM transforms how your organization assesses, improves, and consumes data. Begin your MDM Journey today.