As of November 2026, Latka tracks 25 deep learning software companies with $10M–$100M in annual revenue. They have combined revenues of $848.8M and employ 4.7K people. They have raised $1.3B and serve 116 customers combined.
Every company below sells deep learning software to other businesses and is ranked by its most recent annual revenue. Revenue, funding, headcount and customer figures come from CEO interviews on the Latka podcast, public company filings, and Latka estimates where a company has not disclosed a number.
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.
Sorting: Highest -> Lowest
Showing 10 of 25 companies ranked by annual revenue.
Gothenburg, Sweden
Zenseact’s purpose is to make safe and intelligent mobility real for everyone, everywhere. We live in an ever-changing environment at the center of which autonomous artificial intelligence is about to become reality. Sweden and China based Zenseact is a technology company that is committed to preserving all life on the road through its scalable ADAS/AD software platform.
Shenzhen, China
Developer of a driver assistance system designed to make driving safer. The company's system uses vision algorithm with deep learning technology and performs robustly under complicated conditions of weather, light and traffic, enabling drivers in China to use autonomous driving in bad weather to save from accidents.
Tel Aviv, Tel Aviv, Israel
FuriosaAI designs and develops AI accelerators (NPUs) optimized for data center operations, focusing on high-performance and power-efficient solutions for computer vision, generative AI, and various demanding workloads.
Irvine, California, United States
Syntiant is a leader in edge-AI deployments, bringing deep-learning to any device with industry-leading Neural Decision Processors and hardware-agnostic machine learning models.
Palo Alto, California, United States
Since 2000, InApp has been delivering full-cycle software development services to customers worldwide. Founded by a group of IT experts with several years of Big 5 consulting experience, InApp presently has offices in the USA, India, and Japan; a 400+ strong team of software engineers, and a solid client base ranging from Fortune 500 companies to SMBs. InApp offers an integrated portfolio of software services/technologies such as core technologies, cloud computing, analytics, blockchain solutions, AR & VR Solutions, AI & Deep Learning, and IoT. We deliver our high-performing products to the manufacturing and construction industries and ISVs.
Barcelona, Catalonia, Spain
Develops AI models with spatial intelligence for 3D world perception and interaction
Santa Clara, California, United States
Plus is an AI company whose mission is to build driving intelligence to power a safer and greener world. Plus’s autonomous driving solutions span from driver-out SuperDrive™, to highly automated PlusDrive®, next-gen safety technology PlusProtect™, and model-based perception software PlusVision™. Headquartered in Silicon Valley with operations in the U.S., Europe, and Australia, Plus is named by Fast Company as one of the World’s Most Innovative Companies. Plus’s large AI models are already powering vehicles in commercial use today. Partners including Bosch, dm-drogerie markt, DSV, Hyundai Motor Company, IVECO, Luminar, Nikola, Scania/MAN/Navistar of the TRATON GROUP, and Transurban are working with Plus to accelerate next-generation transportation solutions.
Berlin, Berlin, Germany
Aignostics is an AI-powered precision diagnostics company that focuses on pathology to assist with drug development and clinical research. It specializes in transforming drug development and improving patient outcomes with AI that delivers novel insights for precision medicine.
Toronto, Ontario, Canada
Waabi.ai is an innovative AI technology company focused on revolutionizing the autonomous vehicle industry through cutting-edge automation and machine learning solutions.
Cambridge, Massachusetts, United States
Developer of an artificial intelligence powered life sciences software platform designed to synthesize the exponentially growing biomedical knowledge. The company's platform uses neural networks (shallow and deep learning models) for real-time, automated extraction of knowledge from the scientific, regulatory and commercial body of literature, enabling healthcare enterprises to make biomedical knowledge computable.
Latka tracks 25 deep learning software companies with $10M–$100M in annual revenue. Together they generate $848.8M in annual revenue and employ 4.7K people.
Zenseact is the largest, with $68.7M in annual revenue, founded in 2020.
The average deep learning software company in this list makes $34M a year, across 25 companies with reported revenue. They serve 116 customers combined.
Ranked by annual revenue, the leaders are Zenseact, Minieye, 퓨리오사에이아이, Syntiant Corp and InApp.
The 25 deep learning software companies with $10M–$100M in annual revenue tracked here have raised $1.3B in disclosed funding between them.
- 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