As of October 2026, Latka tracks 7 artificial neural network software companies under $1M in annual revenue. They have combined revenues of $3.2M and employ 28 people.
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 7 of 7 companies ranked by annual revenue.
Balmain, New South Wales, Australia
Our world first phone detection system is able to detect illegal mobile phone use with unrivaled accuracy. Using state of the art video systems One Task is able to provide meaningful traffic analysis for local and state Government. We are experts in the application of neural networks to solve challenging modern problems.
Aachen, Germany
Developer of a distribution management software designed to monitor, analysis, control, automation and planning of distribution grids. The company's active distribution management software utilizes machine learning technique based on artificial neural networks to estimate precisely the state of the power grid using only 10% of processing power compared to a traditional state estimation, enabling distribution network operators to get affordable real-time grid awareness as well as safe and efficient operation to distribution system operators.
Hyderabad, Telangana, India
Redefines infrastructure management with scalable neural network technology, ensuring precise defect detection and maintenance efficiency. Tailored for growth, our platform adapts to your evolving needs, setting a new standard for smart, scalable inspection.
San Francisco, California, United States
Software and hardware to run neural networks faster and cheaper
San Francisco, California, United States
Simpleem is an AI R&D company focused on virtual human interactions. We created Artificial Emotional Intelligence (AEI) that correlates ALL signals (face expressions, body language, voice, text) with business outcomes and guides on how to leverage behavior for success. Our models trained on over 1 Billion Parameters from 10,000+ real business video calls, AEI can identify win/lose patterns and conversation results with extremely high accuracy: 94%. We help our customers to overcome personal connection barriers in remote communication with their clients/partners and improve business performance. Simpleem AEI works like a human and makes sense of various facial expressions by relying on context and past experience. Our mission is to help society achieve positive synergy and enable individuals to navigate through social and communication barriers that seem impassable.
Berlin, Berlin, Germany
Segmentive is a breakthrough video segmentation technology for mobile empowering users to intelligently remove themselves from live backgrounds to be transported into a scene of their choice, creating impossible moments in real-time. CUTTING-EDGE VIDEO SEGMENTATION TECHNOLOGY FOR MOBILE • Despite attempts by major technology platforms, the creation of high-quality green screen style video on smartphone has been unachievable. • Even the most advanced mobile processors lack the architecture and power required, resulting in low -quality visuals that are characterised by unclear edges and disappearing subjects. TECHNOLOGY • Segmentive’s technology combines deep learning techniques with advance neural network algorithms to replicate human understanding of scene identification, on mobile and in real-time. • Deep Neural Network >> Pre-classification of each frame into background and foreground pixels uses a 92-layer, pre-trained Deep Neural Network (DNN). • Generalised Learning Vector Quantization (GLVQ) >> Fine-classification uses GLVQ algorithms to identify and update the characteristics of each frame in real-time which also autonomously learn during run time. • Third Party Information >> Our technology also allows and utilises third party information such as depth sensor from the camera lens to create additional parameters, enhancing the quality of the Segmentation. • Composition >> Software automatically weighs and combines pre-classification and fine-classification by splitting the frame into areas based on classification confidence and visual characteristics. RESULTS The ultimate mixed reality technology for smartphones, Segmentive enables users to seamlessly fuse physical and digital worlds in real-time to create innovative and unique video content that can be instantly shared or streamed in any format. GET IN TOUCH TODAY We are only an email, phone call and Whatsapp away.
Edinburgh, Scotland, United Kingdom
Fabless chip design start-up focusing on next-generation neuromorphic AI chips to accelerate future AI applications
Latka tracks 7 artificial neural network software companies under $1M in annual revenue. Together they generate $3.2M in annual revenue and employ 28 people.
One Task is the largest, with $880K in annual revenue, founded in 2017.
The average artificial neural network software company in this list makes $459.8K a year, across 7 companies with reported revenue.
Ranked by annual revenue, the leaders are One Task, Gridhound, Visnet AI Technology Pvt Ltd., deepsilicon and Simpleem.
- 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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