Technology
AI software and machine learning technology for production analytics.
Artelnics develops core AI products and adapts the right technology stack to each customer environment.
Core products
The company builds and maintains technology for predictive analytics, neural networks, and high-performance AI development.
Neural Designer
Professional software that simplifies the process of building artificial intelligence models with neural networks.
OpenNN
A world-class open-source C++ library that implements neural networks for advanced users.
Custom AI systems
Tailored predictive analytics software designed around customer data, constraints, and deployment context.
Applied capabilities
These are the technical areas most often used in Artelnics projects, from model development to production software.
Machine Learning
Predict outcomes, discover relationships, classify events, and automate decisions with validated models.
Big Data
Prepare and model large, complex datasets from industrial, business, scientific, and operational systems.
Computer Vision
Build systems that interpret images and visual data for recognition, inspection, healthcare, and automation.
Generative AI
Apply modern AI architectures when they improve analysis, productivity, technical workflows, or decision support.
Advanced Software
Design maintainable AI software around data pipelines, model validation, deployment, and user workflows.
Consulting Projects
Turn a technical or business problem into a scoped AI project with data, modelling, and software milestones.
Third-party technologies
The team works with different programming languages, cloud services, database systems, and deployment environments so projects can fit naturally into customer systems.
Cloud and infrastructure
Solutions can be designed for local, cloud, or hybrid environments depending on operational needs.
Data platforms
Models are integrated with existing databases, files, APIs, and analytics systems.
Production deployment
The goal is useful AI that can be maintained, audited, and used after the research phase.