Some tasks need a model trained on your own data
For tasks where general AI models fall short, such as your own document types, product categories or industry vocabulary, we fine-tune or train models on your data and test them against the results you need.
Part of AI and automationWhat we do
Feasibility first
We test general models with careful prompting and retrieval before recommending fine-tuning, because that is often enough.
Dataset preparation
Collecting, correcting and labeling examples from your records, with personal data removed or masked before training.
Fine-tuning
Adapting open or commercial models to your classification, extraction or writing tasks, including Arabic text and Gulf dialect where relevant.
Evaluation on your cases
A held-out test set agreed with you, so the model is judged on your own examples and compared with the general alternative.
Private deployment
Models hosted in a cloud region you choose or on your own servers, so data stays in the environment you approve.
Monitoring and retraining
Quality tracked on live data, with a plan for retraining when products, documents or language change.
When clients call us about this
General models keep getting your terminology wrong
Product codes, trade terms or internal categories are misread, and prompting alone has not fixed it.
Data cannot leave your own infrastructure
Policy or regulation rules out sending records to an external AI service.
A large model costs too much at your volume
The task is narrow and repeated many times a day, and a smaller dedicated model could do it for less.
Questions we are often asked
Do we have enough data?
It depends on the task. The feasibility stage looks at what you have and how much labeling would be needed before any training is planned.
Who owns the model?
You own the trained weights and the dataset prepared from your data, subject to the license of the base model, which we explain before work starts.
Is a smaller model good enough?
Often, for narrow tasks. A smaller model fine-tuned for one job can be cheaper to run and easier to host privately. Evaluation on your test set decides.
How do you handle sensitive training data?
Personal data is removed or masked before training, access to the dataset is limited to named people, and the dataset is stored in the location we agree.
Tell us what you want to build, fix or grow
We reply within one working day with questions or a short plan.