Sophelio Introduces the Data Fusion Labeler (dFL) for Multimodal Time-Series Data

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Sophelio, an award-winning applied AI and machine-learning company, today announced the launch of Data Fusion Labeler (dFL), a data-centric platform designed to help teams organize, label, and prepare complex data streams for machine learning and advanced analysis.

The launch follows Sophelio’s recent name transition and represents a major step in the company’s evolution. dFL was originally developed to meet the demanding data requirements of fusion energy research—where synchronization, data quality, and reproducibility are essential—and has since been expanded into a general-purpose platform for data-intensive, real-world systems.

Following its earlier pre-announcement, dFL is now available, with a beta version live and accessible today. Early users can start using dFL to combine data from different sources, label it, and export it with a clear, traceable history.

dFL is designed for teams working with noisy, asynchronous, and physically meaningful signals, including those in advanced manufacturing, energy systems, robotics, climate science, and applied research. By unifying ingestion, data harmonization, visualization, labeling complex time-series and sensor data, and export into a single workflow, dFL reduces friction between raw data and deployable machine-learning models.

As interest in multimodal time-series data grows, teams are increasingly evaluating a wide range of data labeling and preparation tools. Sophelio recently published an overview of leading approaches and platforms used across the field, highlighting why traditional preprocessing pipelines can quietly fail when applied to real-world sensor data.

“dFL grew out of real-world fusion research, where reproducibility and data integrity aren’t optional,” said Craig Michoski, Co-Founder of Sophelio. “What we’re launching today is a platform that brings those same standards to a much broader set of data-driven applications.”

The technical foundations and performance of dFL are detailed in the paper “The Data Fusion Labeler (dFL): Challenges and Solutions to Data Harmonization, Labeling, and Provenance in Fusion Energy,” now available on arXiv: https://arxiv.org/abs/2511.09725

Data Fusion Labeler is available today, with plans ranging from a free discovery tier to enterprise deployments. Early access to the dFL beta is available at https://dfl.sophelio.io

About Sophelio

Sophelio is an applied AI and machine-learning company focused on transforming complex, high-stakes sensor data into trustworthy, ML-ready datasets. Originating in fusion energy research, the company builds signal-first, provenance-driven data systems that support reproducible analytics and real-world machine learning.