Neural network development
Salto Angel develops neural networks and machine learning systems for a decision you can name: classify, predict, read, or detect — then ships the software that runs the model inside the operation.
When a neural network is the right build
We use neural networks when the pattern lives in examples: text, images, sequences, or tabular history that rules cannot cover cleanly. If a simpler model or a well-scoped language model answers the question, we say so. The goal is a system you can trust on your cases, not the largest architecture available.
What we train and ship
Typical work includes classification, ranking, extraction from documents, forecasting, and vision models. Training is only half of it. The delivery includes evaluation on data you recognize, a way to retrain when the world shifts, and an interface — API, agent, or product screen — so the prediction reaches the person who acts on it.
Data, evaluation, and ownership
We start from the examples you already have and the mistakes that are expensive. Metrics are chosen with you, not borrowed from a paper. Data access stays limited to the job, and you keep a clear account of what the model saw and how a new version is accepted.
The people behind the models
Salto Angel is led by engineers who have built data science, computer vision, and machine learning products end to end. Neural network work sits next to custom AI development, not in a separate lab that never meets the product.
Related products
Frequently asked questions
Do you develop neural networks for a specific company?
Yes. We train and integrate models for your data and your decision, and we also use existing models when training from scratch would not change the outcome.
Is this only deep learning?
No. Machine learning includes simpler models when they are more stable or easier to explain. Deep learning is the choice when the signal needs it, especially in language and vision.
Can you work with limited data?
Often yes: start from a strong base model, label the cases that matter, and measure before expanding the dataset. If the data cannot support the claim, we will not pretend a network will.
How is this different from an AI agent?
A network produces a prediction or a representation. An agent uses that result, plus tools, to carry a conversation or a workflow. Many projects need both.