We select, fine-tune and evaluate small language models on your data so they perform on your tasks, run on infrastructure you control and cost less to operate than general-purpose large models.
When this helps
- A general-purpose model is too expensive, too slow or not accurate enough on your task.
- Your data cannot leave your infrastructure.
- You need predictable cost per request at scale.
- The task is narrow and well defined, so a smaller model can specialise.
How would this work for you?
Answer three quick questions and get a short brief on how we'd approach your model training & slms need.
What's included
- Model selection: SLM, LLM or classical ML
- Fine-tuning and domain adaptation
- Training-data curation and synthetic data where appropriate
- Evaluation against task-specific benchmarks
- Optimisation for on-premise, private-cloud or edge deployment
- Model documentation and hand-over
How we do it
- FrameDefine the task, the success measure and the baseline to beat, usually the model or process you use today.Task definition and baseline
- SelectChoose the approach that fits: a small language model, a larger model, classical machine learning, or no training at all.Model and approach recommendation
- CurateBuild training and evaluation sets from your data, with synthetic data only where it is justified and checked.Training and held-out test sets
- TrainFine-tune and adapt the model, recording every run so any result can be reproduced.Trained model and training records
- Evaluate and deployTest against the baseline on held-out data, optimise for your hardware and hand over with documentation.Evaluation report and deployable model
How we keep quality high
- Test data is held out from training from the start, so results are not inflated.
- Every result is compared with a baseline, including the option of not training at all.
- Training runs are versioned with their data and settings, so results can be reproduced.
- Failure cases are reviewed by people, not only summarised by metrics.
Why Qylis AI
Small where it counts
We favour the smallest model that meets the target, which lowers running cost and keeps deployment simple.
Your infrastructure
Models can be trained and run on-premise or in your private cloud, so your data stays where it is.
Data and training under one roof
The team preparing your data is the team training on it, so nothing is lost at the hand-off.
Honest baselines
If a general model or a simpler method does the job, we show you the comparison and say so.
Team profiles, tools, certifications and references are shared directly by our team, matched to your requirement.
Ways to work with us
Feasibility check
A focused study on your data that shows whether a trained model beats your current option.
Training project
Train, evaluate and hand over a production-ready model.
Model operations
Ongoing retraining and evaluation as your data and needs change.
Every engagement follows the same rhythm: understand, assess, scope, deliver, with evidence against agreed criteria at each step.
Questions
What is a small language model?
A language model with far fewer parameters than general-purpose models, trained or fine-tuned for a narrow set of tasks. It is cheaper to run and can be deployed on your own hardware.
How much data do we need?
It depends on the task and on how different it is from what base models already know. A feasibility check answers this on your data before you commit.
Who owns the trained model?
Ownership is agreed in the contract before training starts, so there is no ambiguity later.
What does Qylis AI's Model Training & SLMs offering cover?
We select, fine-tune and evaluate small language models on your data so they perform on your tasks, run on infrastructure you control and cost less to operate than general-purpose large models. It includes model selection: slm, llm or classical ml; fine-tuning and domain adaptation; training-data curation and synthetic data where appropriate; evaluation against task-specific benchmarks; optimisation for on-premise, private-cloud or edge deployment; model documentation and hand-over.
How does a Model Training & SLMs engagement start?
Understand: We start with the business requirement and the outcome you need to measure. Assess: We review the data, systems and constraints, and confirm where AI makes sense. Scope: We agree scope, acceptance criteria and a first deliverable you can judge. Deliver: We deliver in increments, with evidence against the agreed criteria at each step.
How do I discuss a Model Training & SLMs requirement with Qylis AI?
Send the requirement through the contact form on this page. A solution lead replies within one business day.
Talk to Qylis AI
Tell us the requirement. A solution lead replies within one business day.
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