List of LightlyOne Customers
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Since 2010, our global team of researchers has been studying LightlyOne customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased LightlyOne for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using LightlyOne for ML and Data Science Platforms include: Kiwibot, a United States based Transportation organisation with 150 employees and revenues of $30.0 million, San Diego Supercomputer Center, a United States based Education organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using LightlyOne, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The LightlyOne customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Kiwibot | Transportation | 150 | $30M | United States | Lightly | LightlyOne | ML and Data Science Platforms | 2023 | n/a |
In 2023, Kiwibot deployed LightlyOne in the ML and Data Science Platforms category to automatically select high-value training images from millions of robot-collected frames, supporting its autonomous delivery and robotics operations in the United States. The deployment targeted segmentation model development for production fleets, with a stated focus on improving edge-case discovery and tightening retraining cadence for live robots.
Kiwibot used LightlyOne capabilities for active selection and dataset curation, reducing labeling waste by prioritizing informative and rare frames and enabling a 3× faster model iteration cycle for segmentation models. LightlyOne processed continuous camera frame streams to surface diverse, high-value samples for human labeling and for inclusion in retraining datasets, positioning Kiwibot LightlyOne ML and Data Science Platforms as the training data selection layer for model development workflows.
Operational scope emphasized autonomous delivery teams and production fleet tooling in the United States, with the implementation feeding selected frames into Kiwibot’s labeling and retraining workflows to accelerate model updates. Governance adjustments focused on changing retraining cadence and edge-case monitoring processes to incorporate continuous curated sampling, and the vendor case study explicitly reports reduced labeling waste, faster iteration cycles, and improved discovery of production edge cases.
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San Diego Supercomputer Center | Education | 10 | $1M | United States | Lightly | LightlyOne | ML and Data Science Platforms | 2024 | n/a |
In 2024 San Diego Supercomputer Center deployed LightlyOne within its ML and Data Science Platforms work to curate surgical video frames for instrument detection. The deployment targeted healthcare and surgical video data curation workflows in the United States and was oriented toward supporting YOLOv8 model training and labeling pipelines.
LightlyOne was configured to perform large scale frame selection, automated redundancy reduction, and curated sampling to produce training ready image sets. The implementation documented use of the LightlyOne module to score and select frames, enabling more frequent model iteration and streamlined dataset handoffs to labeling teams.
Operationally the system processed 2.3 million frames in one month and accelerated labeling throughput by approximately 10× for YOLOv8 training, outcomes reported in the LightlyOne case study. The scope focused on surgical instrument detection, affecting data engineering and ML engineering functions responsible for dataset preparation and model retraining workflows.
Governance emphasized dataset curation workflows and iteration cadence, with LightlyOne embedded into labeling and model training pipelines to shorten iteration cycles. The configuration prioritized scalable frame selection and curated dataset delivery consistent with ML and Data Science Platforms capabilities.
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