Domain quality
The model learns your terminology, formats and edge cases: measurably fewer errors than a generic model with a long prompt
Fine-tuning AI models
We adapt models to your domain, your data and your tasks.
A general-purpose model is average in your domain: it stumbles over terminology, breaks the required output format and hallucinates on specifics.
Fine-tuning continues training a model on your data, so it solves exactly your task, knows your terminology and style, and runs on smaller, cheaper hardware.
We do not just train models. We agree on success metrics, compare the result with a baseline model honestly, and bring the model to production: an API service or your own infrastructure.
What we do
We fine-tune language, vision and speech models on your datasets, from LoRA adapters to full training, with quality evaluation and deployment to production.
Language models (LLM)
Domain assistants and generation: support answers, reports, letters and code in your company's style and terminology
Vision and multimodal models
Document classification and data extraction, image recognition, content moderation for your specific cases
Speech models
Call and audio transcription that knows your terminology, product names and accents
We choose the solution for the task, not the other way round: if RAG or a ready-made model is enough, we will say so honestly.
Why it pays off
Privacy and control
Training happens on your data without leaving your infrastructure; the fine-tuned model is your asset, not a subscription
Cheaper production
A compact specialized model often beats a giant general one on your task while running faster and many times cheaper
The difference between a demo and a production system is quality on your data. That is exactly what fine-tuning delivers.
How we work
| Stage | What happens | Result |
|---|---|---|
| Audit | 1-3 days. We define the task, the success metrics and the baseline model to compare against, and assess the data you already have | A clear training plan: format, timeline and expected effect |
| Data preparation | 1-3 weeks. We collect and clean the dataset, label examples and split training and validation sets | A dataset ready for training and quality evaluation |
| Training and evaluation | 1-3 weeks. We train LoRA adapters or full models and compare the candidates against the baseline on your metrics | A model that reliably beats the baseline on your metrics |
| Deployment | 1-2 weeks. We ship the model to production as an API service or on your infrastructure and set up quality monitoring | Live inference plus an update and retraining routine |
Free pilot
We fine-tune a model on a sample of your data before you commit.
We train and evaluate on your sample, compare it with the baseline model and show the numbers, not demo screenshots. If the effect does not pay off, we say so.
When fine-tuning is the right fit
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The task is concrete and its quality is measurable: errors, approval rate, response time
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You have domain data: from a few hundred labeled examples to large archives
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Off-the-shelf models mess up your terminology, output format or style
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Privacy matters: your data stays inside your infrastructure
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You need faster, cheaper inference: a compact specialized model instead of a giant one
How to get started
Describe the task and the data you have. We run the pilot on a sample, compare the fine-tuned model with the baseline in numbers, and tell you honestly whether fine-tuning is needed at all. If it is, we fix the success metrics and start training.
Demlabs — systems that work. Now — tuned to your domain.