Michigan-based AI innovator Progress Inc. unveils a novel comparator based architecture designed to deliver transparent, deterministic, and resource efficient intelligence across various applications.
West Bloomfield Township, Michigan, United States, November 13, 2026 -- Progress Inc., a private artificial intelligence research and intellectual property company founded in Michigan in 2013, today announced the launch of Progress Associative Neuromorphic Comparator (PANC), a proprietary neuromorphic technology designed to compare, recognize, classify, and analyze images, charts, tables, text, and other forms of data through structured associative comparison libraries.
Unlike conventional artificial intelligence systems that depend on deep neural networks and computationally intensive training processes, the platform employs a fundamentally different architecture based on comparative-associative recognition. The system organizes knowledge in associative comparison libraries and generates similarity coefficients between new data and previously stored examples, enabling a broad range of intelligent operations including recognition, classification, clustering, generalization, approximation, interpolation, and extrapolation.
“PANC is built on the principle that comparison lies at the heart of intelligence,” said Vladimir Proseanic, CEO of Progress Inc. “While today’s artificial intelligence systems are largely based on neural architectures introduced nearly decades ago, PANC is founded on a new artificial neuron design and comparator architecture. It provides a universal framework for recognition and reasoning that can be adapted to diverse applications and integrated into existing software systems as an additional intelligent component.”
Traditional artificial neural networks (ANNs) typically rely on gradient-descent training and millions of matrix multiplication operations to adjust model parameters. By contrast, PANC eliminates the need for deep learning during the creation of recognition libraries.
The training process is reduced to two streamlined steps, beginning with converting information from virtually any document or graphical format into the proprietary BCF platform format, followed by combining selected files into organized comparison libraries. The platform also supports automated library construction, including pattern selection, classification, and the establishment of associative relationships, while existing libraries can be updated or expanded without costly retraining of the entire system.
A key differentiator of PANC is its transparency. The comparison process, recognition logic, and classification pathways are accessible for inspection and analysis, allowing technical personnel to review the information and operations that contributed to a given result. Because PANC relies on deterministic comparison processes rather than probabilistic model behavior, it produces fully reproducible results across repeated runs and different computing environments. The framework supports auditing, human oversight, and decision traceability, making it particularly suitable for applications where explainability and accountability are essential.
PANC is optimized for deployment on standard computing hardware, ranging from embedded devices and edge systems to enterprise servers and large-scale computing environments. The architecture aims to offer reduced hardware requirements, energy consumption, training time, and operational costs, alongside seamless interoperability with a broad range of software technologies, including ANN systems, large language models, enterprise databases, and business applications. Furthermore, it supports local execution of intelligent operations that would otherwise require cloud-based processing, helping organizations retain control over sensitive information through a streamlined architecture suitable for implementation in specialized microchips and hardware accelerators.
Progress has successfully evaluated PANC in a variety of real-world applications, including human and object recognition and classification, medical diagnostics, business analytics and decision support, weld defect detection and quality inspection, and other industrial and commercial artificial intelligence applications. The company has also demonstrated effective integration of PANC with I-TRIZ innovation software, enabling the generation of novel ideas, concepts, and solutions across diverse domains of human activity.
The mathematical principles underlying the platform have been presented at international artificial intelligence conferences, and peer-reviewed research describing the comparative recognition methodology has been published by Springer Nature.
Contact Info:
Name: Vladimir Proseanic
Email: Send Email
Organization: Progress Inc
Website: https://progress.ai/
Release ID: 89209551
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Source: MarketersMedia
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