About Us

The best ways to improve the accuracy of machine learning models are to increase the amount of labeled data ingested and/or re-label existing data. Deep Learning models in particular perform best with meaningful training datasets that contain millions to billions of examples for complex machine learning applications, and it takes months and massive amounts of manpower to get them labeled. By the time the data is labeled, it is frequently already outdated. Jaxon labels data in minutes, eliminating this bottleneck and allowing models to be updated continuously.

With self-adjusting pipelines, Jaxon adapts to each organization’s nuanced data and domain-specific terminology. Training sets are created using existing data, as well as new text streaming in from online and internal sources. Jaxon’s Studio allows users to design and curate meta model(s), tune pipelines, and ensemble labelers to optimize training sets. With Jaxon, machine learning applications make more accurate classifications and predictions.

 
 
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 Meet Our Team

 
Scott Cohen CEO

Scott Cohen
CEO

Greg Harman CTO

Greg Harman
CTO

Paul Resten EVP, Sales

Paul Resten
EVP, Sales

Sushil Shelly Director of Engineering

Sushil Shelly
Director of Engineering

 
 
Brad Hatch Principal Data Scientist

Brad Hatch
Principal Data Scientist

Alan Caulkins Machine Learning Engineer

Alan Caulkins
Machine Learning Engineer

Laura Baltazar Sr. QA Automation Engineer

Laura Baltazar
Sr. QA Automation Engineer

Charlotte Ruth Director of Linguistics

Charlotte Ruth
Director of Linguistics

Carly Stithem Director of Marketing

Carly Stithem
Director of Marketing