“Your business runs on data so you better make sure that data is clean.”

Dr. Michael Stonebraker, Tamr Co-founder and Turing Award Winner (2014)

Paving the way to a future where every business runs on clean data

Our journey: charting the path of data innovation

Founded in 2013, Tamr is based on Turing award winner Mike Stonebraker’s research at MIT’s Computer Science and AI Lab. Driven by a shared vision, Dr. Stonebraker and his business partner, Andy Palmer, embarked on a mission to empower companies to unify and master the vast and ever-evolving landscape of highly-diverse data. Today, Tamr remains at the forefront of the quest to improve the quality and trustworthiness of data. Using the perfect fusion of AI and human intellect, Tamr helps businesses uncover new insights from their data so they can work smarter and reach new levels of success.

Our expertise

As the leader in data products, Tamr enables customers to accelerate their time-to-value by delivering validated, trustworthy insights. Tamr’s data products are the first of their kind to unite AI with human intelligence to improve data quality and enrich data with first- and third-party data so businesses can revolutionize customer experiences, drive greater ROI, boost operational efficiency, and avoid risks. Using Tamr’s cloud-native and SaaS solutions, industry leaders uncover the insights they need to stay ahead of the competition in a rapidly-changing business environment.

17 Patents and Counting!

Our rich history of innovation and excellence

Innovation was at the heart of Tamr’s founding at MIT, and it remains a core part of our DNA and company culture. As new standards in scale and efficiency evolve, Tamr continues to lead the market by delivering state-of-the-art technologies that meet the needs of the modern data ecosystem. Our patent portfolio underscores Tamr’s commitment to providing innovation to businesses looking to accelerate their success using accurate insights fueled by clean data.

Tamr’s pioneering, patented technologies help customers:
Answer the questions “what changed?” and “how accurate is my ML model?”
  • Provide automatic, reliable survivorship at scale, enabling the automated creation and maintenance of Tamr IDs 
  • Create a novel, yet straightforward, method of estimating overall accuracy given a very small amount of human input
Curate large, diverse data sets at scale
  • Combine machine learning with human oversight to develop an innovative system that accurately curates data at scale while delivering on the promise of cost effectiveness
  • Pioneer an approach that seamlessly integrates manual data curation into a versioned data product
  • Promote the reusability of human feedback across multiple source data
Capture human feedback in context and use it to train the model
  • Capture user feedback within the context of the application they are using, making it easier for curators to view the feedback in context and fix it
  • Translate user feedback into the input that machine learning models require so that the training remains unbiased, stable, and durable, even when the data or model changes
Make meaningful connections and translate them into active learning
  • Enable the machine to quickly focus on comparable records, making it easier to deduplicate data at scale
  • Use data multiple times in multiple ways, dramatically reducing the amount of training required to achieve a high level of accuracy
  • Enable the machine to quickly focus on meaningful categories when applying the model to find a best-match category for a given record
  • Scale to very large feature datasets by providing alternatives for the use of geospatial databases, even between disparate feature types such as point of interest and building footprint, while avoiding accuracy trade-offs due to projection
  • Translate the technical needs of the machine learning active learning system into practical questions that a data expert can answer