As of August 2026, Latka tracks 19 artificial neural network software companies. They have combined revenues of $202.2M and employ 1.5K people. They have raised $712.6M.
Every company below sells artificial neural network 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.
Artificial Neural Network Software (ANN) refers to a category of applications that utilize algorithms inspired by the human brain's structure and function to process and analyze data. These systems are primarily used in machine learning tasks such as classification, regression, and clustering to drive insights from large datasets. Common use cases include image and voice recognition, natural language processing, fraud detection, and autonomous systems, among others. ANN software is characterized by its ability to learn from data, adapt to new inputs, and improve its performance over time.
Sorting: Highest -> Lowest
Showing 6 of 6 companies ranked by annual revenue.
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.
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.
Seattle, Washington, United States
On a mission to fuel the future of commerce by bringing brands and customers closer together, Stackline is the leading AI-enabled retail intelligence and activation platform for the world's most innovative brands. Business leaders, product innovators, performance marketers, and financial firms trust Stackline as the single source of commerce truth. Fueled by proprietary neural networks and deep learning systems, Stackline's market insights, revenue metrics, behavioral data, and autonomous functionality create the actions that determine success or failure. Founded in 2014 in Seattle, Stackline employs over 250 connected commerce professionals creating value for 7,000 global brands.
Reston, Virginia, United States
Seekr is a trusted AI company that provides a platform to build, train, and deploy AI applications, focusing on accurate, explainable solutions. It empowers enterprises and government agencies to develop reliable AI solutions.
Nantes, Nantes Cedex 3, France
MyScript is the worldwide leader in real-time handwriting recognition for all types of content (text, math, shape, music). Based on neural networks, its technology combines handwriting and artificial intelligence, leading to a natural human-machine interface.
Cambridge, Massachusetts, United States
Liquid AI is a foundation model company headquartered in Boston, Massachusetts, specializing in the development of artificial intelligence solutions, particularly through liquid neural networks.
Latka tracks 19 artificial neural network software companies with reported revenue. Together they generate $202.2M in annual revenue and employ 1.5K people.
Syntiant Corp is the largest, with $62.7M in annual revenue, founded in 2017.
The average artificial neural network software company in this list makes $10.6M a year, across 19 companies with reported revenue.
Ranked by annual revenue, the leaders are Syntiant Corp, nference, Stackline, Seekr and MyScript.
The 19 artificial neural network software companies tracked here have raised $712.6M in disclosed funding between them.
- The software must provide capabilities for creating and training artificial neural networks. - It should support various data types, including unstructured data like images and text. - The solution must enable users to implement supervised, unsupervised, and reinforcement learning techniques. - It should offer tools for model evaluation and performance optimization. - The product must be able to integrate with existing data pipelines and data storage solutions. - Not just provide analytics; it must also allow for real-time predictions and inferences.
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