List of LightlyEdge Customers
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Since 2010, our global team of researchers has been studying LightlyEdge 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 LightlyEdge 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 LightlyEdge for ML and Data Science Platforms include: Kiwibot, a United States based Transportation organisation with 150 employees and revenues of $30.0 million, Rabot United States, a United States based Professional Services organisation with 40 employees and revenues of $10.0 million, Aigen, a United States based Manufacturing organisation with 60 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using LightlyEdge, 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 LightlyEdge 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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Aigen | Manufacturing | 60 | $1M | United States | Lightly | LightlyEdge | ML and Data Science Platforms | 2024 | n/a |
In 2024 Aigen implemented LightlyEdge to curate on-robot imagery for crop and weed detection, positioning LightlyEdge within its ML and Data Science Platforms stack to support computer vision workflows in agriculture in the United States. The deployment focused on embedding LightlyEdge data-selection capabilities into Aigen's robotics data pipeline, aligning application functionality with field level image capture and model preparation processes.
Aigen used Lightly's data-selection engine to preselect and distill robot-collected footage, implementing on-device LightlyEdge inference and selection where feasible to minimize data transfer and labeling overhead. Functional capabilities implemented included automated data selection for class balance and redundancy reduction, sample prioritization for false positive and rare event capture, and export workflows for downstream model training and validation.
Operational integration connected LightlyEdge to on-robot image capture streams and Aigen's existing model training pipeline, enabling continuous ingestion of curated samples from field trials across U.S. sites. The implementation scaled around robot-deployed collection, with iterative selection cycles feeding model retraining and evaluation.
Governance emphasized iterative rollout and data lifecycle controls, with repeated selection and labeling cycles to refine the training corpus and maintain class coverage. The case study reports dataset size reductions of approximately 80 to 90 percent and a doubling of deployment efficiency, outcomes that reflect the narrow scope of on-robot imagery curation and the application of LightlyEdge data-selection workflows to Aigen's crop and weed detection business function.
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Kiwibot | Transportation | 150 | $30M | United States | Lightly | LightlyEdge | ML and Data Science Platforms | 2024 | n/a |
In 2024, Kiwibot deployed LightlyEdge as part of its ML and Data Science Platforms usage to automatically select high-value frames from millions of robot collected images for segmentation model training. The LightlyEdge implementation supported data curation that improved segmentation performance and accelerated model iteration, with Kiwibot reporting a 3x faster model iteration cycle for its autonomous delivery robots in the United States.
The deployment emphasized automated frame scoring and selection workflows consistent with ML and Data Science Platforms capabilities, applying LightlyEdge to prioritize frames that maximize segmentation training value. Configuration centered on dataset curation and active learning style sample selection, enabling engineering and data teams to reduce manual labeling needs and iterate models more rapidly.
Operationally the implementation spanned on device image sampling on Kiwibot robots and downstream integration into training pipelines, creating a closed loop from capture to curated training sets. The case study frames LightlyEdge usage as an extension of Lightly driven data curation workflows to edge devices, aligning the Kiwibot LightlyEdge ML and Data Science Platforms relationship with real time selection and model retraining patterns.
Governance was organized around workflow orchestration between robotics perception teams and data scientists, formalizing recurring curation cycles and labeled data refreshes to sustain segmentation updates. The case study explicitly reports improved segmentation quality and a threefold faster model iteration cadence as outcomes of the LightlyEdge driven data curation approach.
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Rabot United States | Professional Services | 40 | $10M | United States | Lightly | LightlyEdge | ML and Data Science Platforms | 2024 | n/a |
In 2024 Rabot United States implemented LightlyEdge as part of its ML and Data Science Platforms footprint to support packing quality model development in warehouse and fulfillment operations. The deployment applied Lightly selection tooling to packing station video and processed tens of millions of images to produce curated training sets for packing quality across Rabot United States fulfillment sites.
LightlyEdge was configured to perform selection and data reduction on device at the point of collection, reducing the volume of raw video routed into cloud selection workflows while preserving representative frames for model curation. The implemented selection pipeline reduced curation time by approximately 50 percent and doubled onboarding speed for new models, accelerating data labeling and iteration for packing quality systems.
Operational scope targeted packing stations and impacted operations, quality assurance, and data science teams responsible for model training and deployment. Governance emphasized capture to curation policies, instrumented selection rules at the edge, and controlled ingestion into training pipelines, with LightlyEdge and Lightly selection workflows operating in concert across on premise and cloud processing stages.
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Buyer Intent: Companies Evaluating LightlyEdge
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