List of Segments AI Customers
Since 2010, our global team of researchers has been studying Segments AI 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 Segments AI for AI infrastructure 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 Segments AI for AI infrastructure include: Outrider, a United States based Transportation organisation with 200 employees and revenues of $35.0 million, Scythe Robotics, a United States based Manufacturing organisation with 105 employees and revenues of $25.0 million, Ocius Technology Australia, a Australia based Manufacturing organisation with 100 employees and revenues of $15.0 million and many others.
Contact us if you need a completed and verified list of companies using Segments AI, 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 Segments AI 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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Ocius Technology Australia | Manufacturing | 100 | $15M | Australia | Segments.Ai | Segments AI | AI infrastructure | 2023 | n/a |
In 2023, Ocius Technology Australia deployed Segments AI as AI infrastructure to support multi-sensor perception labeling for maritime robotics, specifically to accelerate data workstreams for the iDrogue AUV program. Segments AI was used to combine 2D image annotation with 3D point cloud overlays, enabling object labeling in six degrees of freedom and directly supporting perception dataset creation for autonomous underwater vehicles.
The implementation emphasized annotation tooling and dataset management capabilities within Segments AI, including 2D to 3D fusion visualization, six degrees of freedom annotation workflows, and quality control workflows to increase label consistency. These capabilities were applied by perception engineering and dataset teams to produce training and validation datasets aligned to maritime robotics use cases.
Segments AI supported operational processes around model validation and debugging by surfacing annotated 2D/3D examples and facilitating iterative review of perception outputs. The deployment focused on improving annotation consistency and speeding dataset creation, outcomes explicitly documented by Ocius Technology Australia, and it was used as a core AI infrastructure component in the companys perception engineering pipeline.
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Outrider | Transportation | 200 | $35M | United States | Segments.Ai | Segments AI | AI infrastructure | 2024 | n/a |
In 2024, Outrider implemented Segments AI in the United States as AI infrastructure to support multi-sensor data annotation for autonomous yard operations. The Segments AI engagement focused on producing 2D and 3D ground truth and mapping data to accelerate development of perception models used in yard automation.
Segments AI was configured to manage multi-sensor ingestion and scale annotation workflows, enabling tool-driven labeling for 2D imagery and 3D point cloud mapping. The deployment used Segments AI tooling to orchestrate annotation tasks, reviewer queues, and quality assurance workflows that are standard for AI infrastructure supporting perception pipelines.
Operational execution combined Segments AI tooling with a partnered Africa.ai annotation workforce to produce labeled datasets, the workforce delivering frame level and point cloud annotations to Outrider. The implementation targeted Outrider perception engineering and data science functions within its US operations, integrating labeled outputs into model training and validation workflows for yard automation.
Governance centered on standardized labeling processes, reviewer quality gates, and iterative dataset expansion to support ongoing perception model cycles. The engagement enabled Outrider to scale labeling processes, improve model accuracy for yard automation, and shorten perception model iteration cycles as stated by the company.
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Scythe Robotics | Manufacturing | 105 | $25M | United States | Segments.Ai | Segments AI | AI infrastructure | 2020 | n/a |
In 2020, Scythe Robotics deployed Segments AI to label 2D and 3D sensor data for off-road perception models used in its autonomous commercial mowers. Segments AI, categorized as AI infrastructure, was adopted in the United States to provide a production labeling platform that fed the companys active learning pipeline for perception development.
The implementation centered on image and point cloud annotation capabilities, using ML assisted tools such as Superpixel and Autosegment to accelerate mask creation and object delineation. Scythe configured a taxonomy exceeding fifty classes to capture vehicle specific and terrain specific elements, and applied dataset versioning and quality checkpoints to maintain label consistency across iterations.
Labels from Segments AI were integrated directly into Scythe Robotics active learning workflow, enabling labeled examples to be cycled back into training and selection logic for new annotation tasks. The deployment targeted perception engineering and model training functions supporting the autonomous M.52 mower program in the United States, aligning labeling throughput with model retraining cadences.
Governance focused on standardized labeling guidelines, class review cycles, and annotation quality control to support continuous model iteration. The implementation increased labeling efficiency and quality and supported faster model iteration and deployment for Scythe Robotics autonomous M.52 mowers.
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