Deep learning
Fine-tuning vision, speech and language models on your own data, on rented GPUs or your own hardware, and shrinking them to run where you need them: a server, a phone or a small box next to a camera.
When a general model is not enough.
- General models miss your specifics An off-the-shelf model knows cats and cars, not your products, defects or documents.
- Data cannot leave the building Medical, financial or customer data cannot be sent to a third-party API.
- Cloud AI bills that keep growing Paying per request for a large hosted model becomes expensive at volume.
- Models too big for the device The model works on a powerful server but has to run on a phone or a small edge box.
Models shaped around your data.
- Fine-tuned vision models Defect detection, product recognition and document classification trained on your labelled images.
- Speech and audio models Transcription and audio classification adapted to your accents, vocabulary and recording conditions.
- Private language models Smaller open models fine-tuned for your tasks and run on your own hardware.
- Compression for edge devices Models quantised and pruned to run fast on phones and small devices without a big accuracy loss.
Training a model the careful way.
- Define the target What the model must do, how accurate it must be and where it must run.
- Prepare and label data Collecting, cleaning and labelling examples, with a clear guide so labels stay consistent.
- Start from a strong model A pretrained model is fine-tuned rather than training from scratch, which needs far less data.
- Measure honestly Accuracy on a held-out set that looks like real use, including the hard cases.
- Shrink and deploy Quantisation and export for the target hardware, checked for speed and accuracy.
- Retrain as data grows New labelled examples feed the next version on a schedule.
What you own when training ends.
- Fine-tuned model and its weights
- Training and evaluation scripts
- Accuracy report on real-looking data
- Optimised export for your hardware
- Inference API or on-device package
- Retraining guide
Questions
How do we know fine-tuning beat the original model?
Both are measured on the same held-out examples before and after, and you get the comparison in writing, including the cases each one gets wrong.
Can you work with sensitive data?
Yes. Training can run on hardware you control, data can be anonymised before labelling, and access is limited to the people doing the work.
What hardware will it need to run?
We size it from the speed you need and the number of requests. Some models run on a laptop-class CPU after compression; others need a GPU.
Can the model improve over time?
Yes. Corrections from your team are collected as new labelled examples and used in the next training round on a schedule you choose.