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Machine Learning and AI

At Softinery, we apply machine learning and AI techniques to solve real-world engineering and scientific problems. Our focus is on building reliable, explainable models that work with real data – from sensors, experiments, or production systems.

We combine traditional simulation and modeling with data-driven approaches where physical models are limited, expensive, or incomplete.

Application Areas

Acoustic Analysis

We use neural networks to analyze audio signals recorded during e.g. metallurgical processes. This helps detect anomalies such as material defects, equipment wear, or process deviations – without interrupting production.

Reactor Optimization Using ML

We build models that learn from past data to optimize the operation of chemical reactors. This includes predicting performance, improving stability, and adjusting parameters for higher efficiency.

Generative AI for Engineering Support

We offer expertise in generative models (such as large language models and diffusion-based architectures) for tasks like documentation support, automated report generation, design exploration, and intelligent assistants — tailored for industrial or scientific environments.

What We Provide

  • Custom ML Model Development
    Supervised, unsupervised, and neural models trained on domain-specific datasets.
  • Data Preprocessing and Feature Engineering
    From raw sensor streams, experimental logs, or historical records — cleaned, transformed, and ready for modeling.
  • Deployment as APIs
    We expose trained models via REST APIs or embedded modules, making them easy to integrate into existing software or platforms.
  • Model Validation and Explainability
    Robust testing, visual inspection, and explanation methods to ensure trust in ML predictions.
  • Hybrid Systems
    Integration of ML components into simulation workflows or control systems.

Technologies and Frameworks

  • Python (scikit-learn, PyTorch, TensorFlow, XGBoost)
  • Signal processing (librosa, scipy.signal)
  • FastAPI, Flask, ONNX Runtime for deployment
  • Integration with cloud services or edge environments