About us

AI runs where your business runs – in Java.

Deep Netts is a next-generation AI platform built entirely in Java, designed to bring advanced deep learning directly into the JVM – where most enterprise systems already run.

2022
Founded · grown out of Neuroph
JSR 381
Java standard · reference implementation
2×
Java Champions · on the leadership team
1000×
Faster analysis · U.S. DoE Jefferson Lab

Who we are

AI that fits the enterprise.

We help organizations train, optimize, and deploy AI models without the complexity of bridging to Python or other ecosystems. With GPU acceleration, Vector API integration, and enterprise-grade observability, Deep Netts delivers the performance, scalability, and control that modern businesses demand.

By combining years of research and engineering expertise, Deep Netts enables companies to unlock the full potential of AI with faster training, lower cloud costs, and improved energy efficiency. From scientific research and manufacturing to retail and finance, enterprises use Deep Netts to power intelligent systems that are efficient, secure, and production-ready.

We are proud to collaborate with leading organizations including the U.S. Department of Energy's Jefferson Lab, University of Minnesota, and the Java Community Process, helping advance the next era of Java-native AI.

At Deep Netts, we believe that AI should fit the enterprise – not the other way around.

U.S. Department of Energy – Jefferson LabUniversity of MinnesotaJava Community Process

Our story

From a university lab to the Java standard for AI.

  1. 2004

    Doctoral research on a Java neural network framework – the seed of Neuroph.

  2. 2013

    Neuroph wins the Duke's Choice Award at JavaOne; Zoran Ševarac named Java Champion.

  3. 2017

    JSR 381 Visual Recognition API launched at the JCP, co-led with Frank Greco.

  4. 2022

    Deep Netts Inc founded. JSR 381 final release, with Deep Netts as reference implementation.

  5. 2023

    JCP Member of the Year award for the work on Java and machine learning standards.

  6. 2025

    JavaOne San Francisco: 5000× faster scientific data analysis at Jefferson Lab.

  7. 2026

    Featured in the JavaOne keynote. Deep Netts 4.0 brings FFM and Vector API acceleration.

The idea for Deep Netts was born in 2022, while working on Neuroph, an education software for neural networks. After achieving strong results on this project with his university team, Deep Netts' co-founder Zoran Ševarac was inspired to seek a similar solution for the enterprise environment.

Recognizing the challenges enterprises face in integrating AI into their Java systems, Zoran envisioned a platform that facilitates seamless AI integration, transforming high-fixed-cost solutions into scalable operational expenses.

Zoran Ševarac, a Java Champion, esteemed professor at the University of Belgrade, and AI researcher, is a Duke's Choice Award winner with over 20 years of experience in AI and software development, and co-author and expert group co-lead of the Visual Recognition API (JSR381) standard for machine learning-based image recognition that determines how Java applications talk to AI.

With his passion and deep understanding of neural networks, deep learning, and software engineering, combined with his extensive experience leading teams of experts, Zoran has managed to bridge the gap between AI complexities and the needs of Java developers across various sectors, including business, academia, and government. As presented at the JavaOne 2025 Conference in San Francisco, Deep Netts has brought a breath of fresh air to the Java community, empowering modern Java for the AI revolution.

But Deep Netts isn't just about technology; it's a purpose-driven company committed to accelerate implementation of AI in the world by empowering major industries and sectors in the AI race, while advancing democratization and positive societal impact of AI.

“Deep Netts is definitely the best starting place for someone trying to implement platform-independent neural networks.”
Dr. Gagik Gavalian, Staff Scientist, Jefferson Lab · Read the case study
1000×
Faster processing
+12–15%
Track reconstruction efficiency
+15–35%
More physics statistics

Recognition

Recognised by the Java community.

  • 2013
    Oracle
    Duke's Choice Award

    Oracle's award for the most innovative software on the Java platform, for Neuroph.

  • 2018
    Java Community Process
    JSR 381 – Most Significant JSR

    JCP recognition for bringing a standard machine learning API to Java.

  • 2023
    Java Community Process
    JCP Member of the Year

    Awarded to Zoran Ševarac and Frank Greco for JSR 381.

Get in touch.

Contact us
Deep Netts Inc
16192 Coastal Highway
Lewes, Delaware 19958, USA
Deep Netts DOO
Jevremova 41
11158 Belgrade, Serbia
Contact
contact@deepnetts.com