List of Apple Cocoa Touch (iOS) Customers
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Since 2010, our global team of researchers has been studying Apple Cocoa Touch (iOS) 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 Apple Cocoa Touch (iOS) for AI Frameworks and Libraries 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 Apple Cocoa Touch (iOS) for AI Frameworks and Libraries include: Memrise, a United Kingdom based Education organisation with 70 employees and revenues of $35.0 million, HomeCourt, a United States based Media organisation with 100 employees and revenues of $20.0 million, Polarr, a United States based Media organisation with 30 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Apple Cocoa Touch (iOS), 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 Apple Cocoa Touch (iOS) 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 | Insight Source |
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HomeCourt | Media | 100 | $20M | United States | Apple | Apple Cocoa Touch (iOS) | AI Frameworks and Libraries | 2018 | n/a | In 2018, HomeCourt implemented Apple Cocoa Touch (iOS) in the Apps Category. The implementation delivered an AI powered basketball training application that provided real time shot tracking and advanced shot metrics to users in the United States, and the app was demonstrated at Apple’s September 2018 iPhone event. The technical implementation centered on on device machine learning for video analysis, inferred to use Apple frameworks such as Core ML and Vision based on App Store feature notes and team references to device accelerated ML. Apple Cocoa Touch (iOS) served as the application runtime and SDK surface for camera capture, real time pose estimation, shot detection, and metric extraction modules, with model conversion and optimization targeted to device neural engines. Operationally the deployment emphasized client side processing to enable low latency Shot Science, leveraging the A12 Bionic chip for device accelerated inference and real time scoring. Distribution and updates were managed through the App Store, supporting product engineering, mobile development, data science, and UX teams focused on maintaining model versions and application performance across iOS device families. Governance and rollout details included a public demonstration at Apple’s event followed by staged App Store releases and platform specific performance tuning, with model packaging and app release pipelines coordinated by the mobile engineering team. The implementation explicitly relied on Apple Cocoa Touch (iOS) and on device ML frameworks to deliver real time shot tracking and advanced shot metrics, with the A12 Bionic cited as an enabling hardware capability for real time Shot Science. | |
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Memrise | Education | 70 | $35M | United Kingdom | Apple | Apple Cocoa Touch (iOS) | AI Frameworks and Libraries | 2018 | n/a | In 2018, Memrise implemented Apple Cocoa Touch (iOS) using Apple's Create ML and Core ML tooling to build on device image recognition models for interactive learning features. This implementation targeted the Apps Category . The deployment centered on training image recognition models with Create ML, converting and packaging models to Core ML format, and embedding Core ML inference calls within the Apple Cocoa Touch (iOS) app runtime. Functional capabilities included image classification for interactive exercises, local model inference to support offline learning, and model footprint optimization to fit mobile constraints. Integrations were scoped to the iOS application stack, with Core ML models invoked from Cocoa Touch UI and application logic, and model artifacts distributed as part of the app bundle. Operational coverage included product and engineering delivery for the Memrise mobile app used by customers in the United Kingdom and worldwide. Governance emphasized application-level model packaging and release control, with iterative retraining workflows executed via Create ML to produce progressively smaller Core ML models. The reported outcomes included a reduction in model training time from approximately 24 hours to under an hour, and a model footprint reduction example from about 90MB to roughly 3MB, enabling practical on device inference for end users. | |
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Polarr | Media | 30 | $10M | United States | Apple | Apple Cocoa Touch (iOS) | AI Frameworks and Libraries | 2018 | n/a | In 2018 Polarr implemented Apple Cocoa Touch (iOS) and used Apple's Core ML to run compressed, on device neural networks that power photo editing and style transfer features. Apps Category Polarr integrated Apple Cocoa Touch (iOS) at the application layer to host Core ML models, emphasizing model compression and runtime optimizations to enable interactive editing workflows within the mobile app. The implementation focused on embedding compressed neural network models for photo enhancement and style transfer, enabling real time inference up to ~20 FPS in lab tests as reported by VentureBeat. The architecture centers on on device inference using Apple Cocoa Touch (iOS) runtime and Core ML model packaging, with explicit operational tradeoffs noted for thermal and battery consumption during sustained use by mobile photography users globally. |
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