List of Figure Eight Platform Customers
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Since 2010, our global team of researchers has been studying Figure Eight Platform 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 Figure Eight Platform 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 Figure Eight Platform for ML and Data Science Platforms include: Home Depot, a United States based Retail organisation with 470000 employees and revenues of $159.51 billion, Adobe, a United States based Professional Services organisation with 31360 employees and revenues of $23.77 billion, Spotify, a Sweden based Media organisation with 9123 employees and revenues of $3.19 billion, eBay, a United States based Retail organisation with 11500 employees and revenues of $2.60 billion, Here Technologies, a Netherlands based Professional Services organisation with 9000 employees and revenues of $1.01 billion and many others.
Contact us if you need a completed and verified list of companies using Figure Eight Platform, 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 Machine Learning software purchases.
The Figure Eight Platform 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 Machine Learning 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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Adobe | Professional Services | 31360 | $23.8B | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
In 2016, Adobe deployed the Figure Eight Platform to create high-quality training data for Adobe Stock search and discovery. Adobe Stock contains over 120,000,000 photos, diagrams, videos, and graphics with roughly 150,000 new uploads each day, and Adobe required annotation at scale to surface nuanced image attributes that uploader metadata does not capture.
The Figure Eight Platform was configured to run polygon annotation workflows and human-in-the-loop labeling tasks, enabling annotators to draw polygons over copy space and similar features and to mark object isolation characteristics. These functional capabilities produced curated, high-accuracy labeled datasets that informed computer vision model training, and the narrative explicitly references the Figure Eight Platform as the annotation engine.
Annotated outputs were consumed by Adobe’s model training pipelines and applied to Adobe Stock search and discovery, extending coverage across the entire catalog and incoming daily uploads. The implementation impacted product search and content discovery functions and supported use cases for marketing customers who need images optimized for overlaid text and clean composition.
Governance combined human labeling with existing automated metadata and color attribute extraction, using human-in-the-loop processes to complement rather than replace algorithmic signals. The result is models that better surface images with attributes like copy space and object isolation, enabling users to find highly useful assets more quickly and accelerate marketing content creation.
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Blue River Technology | Manufacturing | 80 | $17M | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2017 | n/a |
In 2017, Blue River Technology implemented the Figure Eight Platform as part of its ML and Data Science Platforms adoption to support high-fidelity labeling for its See & Spray product line. See & Spray augments self-propelled sprayers with on-board computing and vision algorithms to distinguish crops from weeds across large-scale farms, addressing the operational inefficiency of blanket herbicide application. The implementation targeted the core problem of costly and imprecise spraying, enabling per-plant decisioning in the spraying workflow.
Blue River used the Figure Eight Platform to generate pixel by pixel labeled images, creating training datasets for its computer vision models. The platform supported annotation workflows that identified specific plant types such as cotton and pigweed, and those labeled datasets were consumed by Blue River’s model training pipeline. The Figure Eight Platform processed real-world field imagery supplied by Blue River to produce iterative datasets for model refinement.
Operationally, imagery was routed from field-mounted cameras on See & Spray machines into the Figure Eight Platform, then the labeled outputs were ingested into Blue River’s model training and validation systems. Trained models were deployed to on-board computing units on the sprayers to perform real-time inference and precision actuation of spraying hardware. The solution connected field operations with engineering and data science functions across large-scale farm sites.
The deployment established an iterative feedback loop where field teams continuously upload new images and Blue River expands label taxonomies to include new weed species, supporting frequent retraining cycles and dataset versioning. Governance centered on label taxonomy management and controlled model releases to shift operational procedures from blanket spraying to model-driven selective spraying. Processes were restructured to prioritize field data collection, annotation workflows, and operational integration of model outputs into actuation systems.
Outcomes documented in the case include a reported 90% reduction in average herbicide spend and the enabling of non-GMO seed use that costs about half the price of treated seeds. The Figure Eight Platform enabled species level identification so sprayers can avoid applying ineffective herbicides on resistant weeds. Reduced herbicide usage was also noted for its environmental benefit, lowering chemical runoff into the water table.
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CrowdReason, LLC | Professional Services | 10 | $1M | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
In 2016, CrowdReason, LLC deployed the Figure Eight Platform as part of an effort to automate extraction from property tax forms across more than 20,000 U.S. taxing jurisdictions, leveraging ML and Data Science Platforms to scale document processing. The Figure Eight Platform was used to operationalize human-in-the-loop labeling and automated task routing alongside CrowdReason’s in-house OCR and feature extraction capabilities.
Implementation centered on an OCR model that reads scanned documents and a fingerprinting module that characterizes a document by extracted features to determine prior occurrences. Documents identified as unseen are routed through the Figure Eight Platform where a series of human tasks extract multiple data points, and each extracted field feeds an extraction algorithm that optimizes bounding boxes and template definitions over time.
The solution is integrated into CrowdReason’s core product workflow, with bots applying preloaded algorithms to make unstructured decisions and with human workers resolving low confidence items before aggregated results are delivered to the customer. Operational coverage focuses on document ingestion, feature fingerprinting, field extraction, and downstream delivery to tax processing workflows for CrowdReason customers managing multi-jurisdictional liabilities.
Governance and workflow transformation emphasized iterative model training and confidence thresholds, moving from an initial 100 percent manual extraction process to a model where humans are required for less than 40 percent of data extraction. Over time the automated templates and bounding box performance improve so that more than 60 percent of scanned document data is extracted without human input, with the stated goal of ultimately recognizing and extracting data with no human intervention.
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Retail | 11500 | $2.6B | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 10 | $1M | Hong Kong | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
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Professional Services | 9000 | $1.0B | Netherlands | Appen | Figure Eight Platform | ML and Data Science Platforms | 2017 | n/a |
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Retail | 470000 | $159.5B | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
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Media | 9123 | $3.2B | Sweden | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
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Buyer Intent: Companies Evaluating Figure Eight Platform
- City of Barnesville, GA, a United States based Government organization with 50 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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