Products & ServicesCustom Model Training
AI models trained and tested on your own data and your own cases, not on a public ranking test.
How it works
Clean your data
Your records and documents become one clean training set.
Label what needs labelling
Examples are tagged so the model knows what they are.
Test on your real cases
The model is judged on cases you pick, not on a public ranking.
Illustration only. Names and figures are made up.
Read the full description
A model trained on general data often misses what matters in your business. We start from your business records and documents. First we clean them. Then we label what needs labelling, which means tagging examples so the model knows what they are. Next we train the model. Finally we test it on real cases you pick.
We prefer the simplest approach that works. Often a ready-made AI model plus your data does the job. Nothing new needs to be trained. For example, our own media QC tool checks the quality of images and video. It runs on off-the-shelf vision models. We plan a small trained add-on only for cases where those models fall short. Our data analysis engine lets the database do the counting. It uses language models (AI that reads and writes text) only to explain results. So the numbers can be checked.
After launch, we keep watching how the model performs. So you can see when it needs retraining.
What is included
Data sourcing and rights
We confirm what data you have and where it came from. We also check whether it can be used for training.
Cleaning and labelling
Remove duplicates and errors. Then label the examples the model needs to learn from.
Training and fine-tuning
We train a new model or adjust an existing one, which is called fine-tuning. It depends on what your data supports.
Testing on your real cases
We measure the model on cases you choose. So the results reflect your actual work.
Deployment with monitoring
Put the model to work and track how it performs over time.
Key strengths
Data work comes first
We also build data cleaning pipelines, which are sets of steps for cleaning data. So the training data is in good shape before training starts.
No training for its own sake
If an existing model already solves the problem, we tell you. Then we use it.
Checkable outputs
Where numbers matter, the calculation stays in code or the database. It does not happen inside the language model.
What we need from you
Representative examples, labels where available, data-use permissions and the cost of a wrong result.
Deliverables
- A reviewed dataset and labelling guide
- A baseline comparison and independent evaluation report
- A model or adaptation only when evaluation supports it
How it is accepted
Agree the evaluation split and error categories before training; compare against a simpler baseline on unseen examples.
Case studies
Start with one small pilot.
Tell us the task. We will say whether it is worth doing.