Custom AI Models Trained on Your Data
We train smaller, focused models on your own datasets so they run faster, cost less, and stay fully private.
By the numbers
10–30 sec/doc
on an untrained general model
6x faster
inference speed-up
6-10 weeks
average from dataset to deployed model
100% self-hosted
no data leaving you
Let's scope your model.
A short technical review is enough to estimate data, labeling, and infrastructure requirements.
Why train a custom model at all
Large general models are expensive to run
A 90B-parameter model needs 128GB+ RAM with 4-bit quantization (or 180GB+ unquantized) and a powerful GPU, and still takes 10-30 seconds per document
Training in the cloud burns budget fast
One client built a training instance that cost €60,000/month, running it only a few hours at a time to bring real cost down to a few thousand euros.
A focused model needs far less to run
Once trained on your dataset, a smaller model (13B instead of 90B) can run on a laptop or a standard cloud instance
Public models aren't an option for sensitive data
Companies handling financial, legal, or healthcare documents can't send that data to third-party APIs without extra DPAs, SCCs, and audit overhead under GDPR. A private, self-hosted model removes that requirement entirely
Quality data labeling is the foundation
Before training, datasets need structured labeling: bounding boxes, classes, edge cases across lighting and angle.
Custom models adapt to new document types
A private model can be retrained as new patterns appear without rebuilding the whole pipeline.How we train a custom model
Case Study
Custom dataset labeling & model training for an AI model - Region Norway
The situation
A client building a custom AI model needed labeled data specific to their use case, since generic pre-labeled datasets didn't match the categories the model needed to learn — early tests topped out around 70% accuracy
What we built
A labeling workflow built around the client's 18 categories and dge cases (lighting, angle, document quality) Quality control built into the pipeline from day one — inter-annotator agreement above 95% Dataset delivered in the client's required training structure Labeling capacity scaled with dataset size, no process rebuild needed
22,000+ images
labeled across 18 categories95% accuracy
on production data7 weeks
from dataset to deployed model97%
inter-annotator agreement throughout labeling
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