Train AI Models on Your Own Data
Off-the-shelf models miss your products, documents, defects or language, and sending sensitive data to third-party APIs is not always an option.
A model trained or fine-tuned on your data — with a clean dataset, accuracy measured on your own test set, and a production deployment you fully own.
Typical timeline: 3-8 Weeks.
Capabilities
- Dataset collection, cleaning & labelling
- LLM fine-tuning (LoRA / QLoRA) on your domain data
- Custom vision models: detection, classification, segmentation
- Face, OCR and speech model fine-tuning
- Evaluation on your own test set & model comparison
- Quantisation and deployment to cloud, on-premise or edge
What you receive
- Curated & Versioned Training Dataset
- Trained Model Weights You Own
- Evaluation Report on Your Test Set
- Inference API & Deployment Scripts
Technology
PyTorch · Hugging Face Transformers · LoRA / QLoRA · YOLOv8 · Weights & Biases · ONNX · TensorRT · vLLM
Frequently asked questions
- When should we train or fine-tune our own model instead of using ChatGPT or another API?
- When the task depends on knowledge or patterns general models do not have (your products, defects, documents, language or tone), when data must stay private, or when per-request API costs get too high at your volume. Often retrieval (RAG) is enough — we tell you which approach fits before you spend anything on training.
- How much data do we need?
- It depends on the task. Fine-tuning a language model can start from a few hundred to a few thousand good examples; a custom vision model usually needs a few hundred labelled images per class. We assess your data during scoping and can help collect and label more.
- Who owns the trained model?
- You do. Model weights, training code, dataset versions and evaluation results are handed over in full — no lock-in.
- Can the model run on our own servers?
- Yes. We deploy to your cloud account, to on-premise GPU servers, or quantised for edge devices, behind an API your systems call.