Technology

At NeuralSpike we specialize in delivering custom AI solutions for our clients. To deliver the highest possible value, we also invest heavily in developing our own intellectual property, tools, and frameworks. This enables us to speed up development, improve the quality of our solutions, and reduce overall costs. By having the right tools tailored to our workflows, our team can focus on solving the core challenges that matter most to our clients, rather than spending time on repetitive boilerplate code. This approach allows us to deliver smarter, faster, and more efficient AI systems that drive real impact.

ML-Toolbox

At the heart of our AI development is our custom ML-Toolbox - a powerful and flexible machine learning framework designed to streamline and optimize model training. Our deep learning framework is built on top of well-established open-source libraries like PyTorch and PyTorch Lightning. By leveraging these robust tools, we ensure reliability and ease of extension, making it simple for developers to build on top of a solid foundation.

The framework is modular by design, which makes integrating with other tools and ecosystems effortless. For instance, we provide native support for MLflow, Neptune, and Weights & Biases for training monitoring. Thanks to the extensible architecture, incorporating additional monitoring solutions requires minimal effort.

Similarly ml-toolbox provides an efficient and flexible data loading pipeline. It supports a wide range of annotation formats. But also allows users to develop their own plugin data loaders for custom formats. These custom loaders automatically inherit all the benefits of our data pipeline, such as caching, data verification, and parallel loading, ensuring high performance and scalability across different workflows.

Our toolbox allows swapping components like: dataloaders, models, optimizers, and many more, reducing development time.

Our ml-toolbox features can be summarized with the list below:

Efficient data loading

AI model architecture search

Built-in hyper parameter tuning

Knowledge distillation

Quantization

Various export targets (embedded, web etc.)

Pre-trained models catalogue (for quick development)

Modular - easy to integrate architecture

Compatible with popular open-source libraries

When starting a new machine learning project, a common challenge is the amount of time spent reimplementing the same training loop logic, handling boilerplate code, and wiring together basic components. Our framework addresses this by providing a well-tested, production-ready foundation that has been developed and optimized over time. It comes packed with rich features out of the box, allowing teams to hit the ground running without reinventing the wheel. By abstracting away the repetitive setup and offering robust, modular components, it enables teams to focus on the parts of the codebase that actually drive innovation and make a difference in the project.