List of Figure Eight Platform Customers
Appen
9 Help Street Level 6,
Sydney, 2067, NSW,
Australia
Sydney, 2067, NSW,
Australia
61 2 9468-6300
Professional Services
1386
$247M
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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eBay | Retail | 11500 | $2.6B | United States | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
In 2016, eBay implemented the Figure Eight Platform as part of its ML and Data Science Platforms effort to operationalize human-in-the-loop labeling for product taxonomy and GTIN enrichment. The deployment targeted the company catalog function that manages millions of product taxonomies originating from global seller content, using the platform to augment automated classification with human annotation at scale.
The Figure Eight Platform partitioned large datasets into microtasks that were completed online by thousands of contributors, with the platform orchestrating task distribution, result aggregation, and quality checks. A purpose built machine learning workflow presented contributors with a Product Image and a Product Title and between one and six possible classifications, the order of which was randomized to reduce first response bias, and the workflow was retained for ongoing classification jobs to continuously improve the classification model.
For GTIN discovery, Figure Eight implemented an exhaustive search workflow where contributors used product title, product type, and image to locate 12 to 14 digit GTINs across reliable channels, and the platform compared and reconciled contributor responses to verify each identifier. A geographic filter was applied to prioritize native English speaking contributors aligned with eBay core customer knowledge, concentrating operational coverage on contributors whose language familiarity improved taxonomy accuracy.
Governance and quality control were managed by Figure Eight through workflow instrumentation and multi-contributor verification, turning a previously outsourced offshore model that had cost, scalability, and accuracy challenges into a controlled human-in-the-loop data pipeline. eBay completed more than 15 distinct high value projects on the platform over the prior year, and the collaboration produced faster, lower cost, and higher accuracy classification and GTIN enrichment outcomes that remain embedded in ongoing catalog operations.
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EMOS | Professional Services | 10 | $1M | Hong Kong | Appen | Figure Eight Platform | ML and Data Science Platforms | 2016 | n/a |
In 2016, EMOS implemented the Figure Eight Platform to build human-in-the-loop labeling workflows aimed at improving emotion detection in conversational audio. EMOS leveraged the Figure Eight Platform as an ML and Data Science Platforms solution to address the gap between academic datasets and real-world call center audio, aiming to produce training data aligned with their call center use cases.
The implementation centered on annotation pipelines for audio clips, including workload configuration for annotators, label schema for emotions, and consensus-based quality controls. EMOS configured workflows that captured emotion labels and intensity metadata, enabling downstream supervised training and iterative model refinement in their emotion detection algorithms.
Operational coverage focused on single-speaker selections from call center conversations to reduce ambiguity, and on prioritizing strongly expressed emotional samples where annotator agreement was highest. The deployment supported business functions in customer service and sales analytics, as the Figure Eight Platform processing directly fed labeled examples used to retrain EMOS models for conversational audio.
Governance changes included redesigned annotator instructions, sample selection rules that filtered multi-speaker segments, and prioritization heuristics for high-intensity emotion rows to create definitive training exemplars. EMOS instituted annotation-level quality checks and agreement thresholds to manage subjectivity and to standardize labeling across the small internal team.
The outcome was explicit, EMOS achieved 80% accuracy in their emotional detection algorithms, an improvement of 30 percentage points over prior model performance that had been training on academic datasets. The higher-precision models enabled EMOS to provide clients conversation-level emotional insight for customer service and sales without exhaustive manual auditing of individual calls.
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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
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating Figure Eight Platform. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating Figure Eight Platform for ML and Data Science Platforms include:
- City of Barnesville, GA, a United States based Government organization with 50 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD Figure Eight Platform Coverage
Figure Eight Platform is a ML and Data Science Platforms solution from Appen.
Companies worldwide use Figure Eight Platform, from small firms to large enterprises across 21+ industries.
Organizations such as Home Depot, Adobe, Spotify, eBay and Here Technologies are recorded users of
Figure Eight Platform for ML and Data Science Platforms.
Companies using Figure Eight Platform are most concentrated in Retail, Professional Services and Media, with adoption spanning over 21 industries.
Companies using Figure Eight Platform are most concentrated in United States, Sweden and Netherlands, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of Figure Eight Platform across Americas, EMEA, and APAC.
Companies using Figure Eight Platform range from small businesses with 0-100 employees - 37.5%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 25%, and global enterprises with 10,000+ employees - 37.5%.
Customers of Figure Eight Platform include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified Figure Eight Platform customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.