Houston, March 3, 2025 – 3LC, a leader in data quality and optimization for machine learning, announces a new agreement with Equinor. This marks another milestone in 3LC’s continued expansion as multiple enterprises adopt their platform to enhance machine learning workflows.
3LC’s data optimization platform provides an advanced, scalable approach to improving data quality for AI models. By integrating seamlessly into existing cloud environments without requiring data movement or duplication, 3LC enables enterprises to iteratively refine their datasets by identifying challenging samples, understanding where models struggle during training, improving label consistency, balancing datasets, and leveraging model insights to enhance AI performance across workflows.
With its growing adoption, 3LC has helped organizations simplify data improvement through:
- Seamless Deployment – Integrating directly within enterprise infrastructure to enable real-time dataset enhancements.
- Optimized Data Quality – Leveraging advanced weighting and model-assisted labeling to refine training datasets.
- Improved Model Feedback Loops – Providing insights that allow teams to iteratively improve training data and model performance without disrupting workflows.
As 3LC continues to sign agreements with global companies across multiple industries, its platform is becoming a critical tool for teams looking to enhance their AI performance with minimal operational overhead.
“Machine learning teams are recognizing that improving training data quality is one of the most effective ways to achieve better AI models” said Paul Endresen, CEO of 3LC. “The expansion of our platform’s adoption reflects the value organizations see in our approach to simplifying and optimizing machine learning workflows.”
About 3LC
3LC provides a powerful data quality and optimization platform designed for machine learning workflows. By enabling seamless data integration, advanced model feedback metrics, and actionable insights, 3LC helps organizations improve model performance efficiently and at scale.
