AI Fundamentals

Neural Network

A neural network is an AI model that — inspired by the human brain — consists of interconnected layers of artificial neurons. Each neuron processes inputs, applies weights, and passes on a result. Through training on data, these networks learn to recognize patterns and make predictions.

Why does this matter?

Neural networks are the technical foundation behind virtually all modern AI applications — from image recognition in quality control to text processing in customer support. Decision-makers need not understand the details, but should know: the more high-quality data available, the better these systems perform.

How IJONIS uses this

We leverage pre-trained neural networks (foundation models) and adapt them to your use case — instead of training expensive models from scratch. For specialized tasks like anomaly detection or image classification, we use lightweight architectures that run on edge hardware.

Frequently Asked Questions

Does my company need its own neural network?

In the vast majority of cases, no. Pre-trained models (foundation models) cover 90% of use cases and only need adaptation via fine-tuning or RAG. Training a custom network from scratch only makes sense for very specific, data-rich scenarios.

How much data does a neural network need?

This varies greatly. An LLM is trained on billions of texts, but thanks to transfer learning and fine-tuning, a few hundred examples often suffice for your adaptation. Data quality is decisive: clean, representative data beats massive, noisy datasets.

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