
Toronto, Ontario, Canada
Tenstorrent is a next-generation computing company that builds computers for AI.
- Revenue
- $501.6M
- Customers
- -
- Year founded
- 2016
- Funding
- $793M
- Team size
- 1.1K
- Growth
- -
As of May 2026, there are 178 SaaS companies in Deep Learning Software. They have combined revenues of $3.3B and employ 16.8K people. They have raised $6.7B and serve 557.1K customers combined.
Deep Learning Software refers to a category of applications that leverage deep learning techniques to analyze data, automate processes, and derive insights. These tools use artificial neural networks to mimic human cognitive processes, allowing for complex computation and pattern recognition. Common use cases include image and speech recognition, natural language processing, and predictive analytics across various industries such as healthcare, finance, and technology. Typical features of deep learning software include data preprocessing tools, model training capabilities, performance optimization, and deployment functions. Users often span a wide range of professions, including data scientists, IT professionals, and analysts, who apply these technologies to derive actionable insights from large datasets. As the technology matures, industries are increasingly adopting these solutions to enhance decision-making processes and drive innovation in their operations.
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Showing 10 of 178 companies ranked by annual revenue.

Toronto, Ontario, Canada
Tenstorrent is a next-generation computing company that builds computers for AI.

Munich, Germany
Agile Robots is a world-class provider of next-generation automation solutions based in Munich, focused on developing intelligent robotic systems by combining artificial intelligence and robotics.

London, Greater London, United Kingdom
Wayve is pioneering Embodied AI technology for autonomous vehicles, developing AI that empowers vehicles to perceive, predict, and navigate dynamic environments.

Beijing, China
Megvii is a Chinese technology company that designs image recognition and deep-learning software. It is widely known for its Face++ system and provides AI and IoT solutions.

Irvine, California, United States
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate safely in industrial environments. They specialize in artificial intelligence solutions for autonomous robots across various sectors.

Haymarket, New South Wales, Australia
Harrison.ai is a global healthcare technology company on a mission to urgently scale healthcare capacity through AI-powered medical imaging diagnostic support. The company is clinician-led and leverages deep learning to improve healthcare outcomes.

Zurich, Switzerland
Developer of computer vision applications designed to reinvent how enterprises and consumers interact with everyday objects and augmented reality. The company's enterprise technology platform for mobile computer vision, barcode scanning and augmented reality, combines deep learning and machine learning with more traditional computer vision heuristics, and extends the Internet of Things to everyday objects, enabling enterprises to identify, track and superimpose relevant digital information on these objects without requiring them to have a computer chip embedded or be connected online.

San Francisco, United States
Nanonets leverages advanced OCR and Deep Learning technology to efficiently extract relevant information from unstructured text and documents. It enables the digitization of documents, extraction of specific data fields, and facilitates integration with everyday applications through APIs, all within a simple and intuitive interface. This technology significantly streamlines manual processes by automating tasks such as invoice, receipt, and document reviews. It notably reduces processing time by up to 90% and can save up to 50% on costs.

London, England, United Kingdom
Synthesia.io is an innovative technology company specializing in artificial intelligence and deep learning solutions for video synthesis and content creation.
- Must provide tools for building, training, and deploying deep learning models - Should include support for various data types, such as text, images, and audio - Must offer capabilities for model evaluation and performance metrics - Should enable integration with big data frameworks and cloud services - Not just focused on traditional machine learning; must specifically address deep learning methods - Must facilitate automation of repetitive tasks within the deep learning workflow - Should be suitable for use by professionals such as data scientists, engineers, and researchers
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