AI Training: Models & Teams

Problems this solves

• General-purpose AI models don't handle your documents, terminology or formats well enough.
• API costs or data-privacy rules make large hosted models impractical.
• You can't tell whether a custom model would actually beat an off-the-shelf one.
• Your team uses AI tools inconsistently — or avoids them because nobody showed them how.

Training models

Fine-tuning is not always the answer

Many problems that look like they need a custom model are solved more cheaply by better prompting, or by retrieval that gives a general model access to your documents. We test the simplest option first and only recommend training when it clearly beats the baseline on your own examples — measured, not assumed.

When training does make sense

• A smaller model needs to perform one task reliably — extraction, classification, summarization in a fixed format — at lower cost or on your own hardware.
• Your terminology, document formats or output structure are too specialized for general models.
• Data-privacy rules mean the model must run inside your infrastructure.
• You have structured data and a prediction task — classification, risk scoring — suited to classical machine learning.

How we train

We prepare training, validation and test data without leakage between them, fine-tune open models or train classical models, and compare every result against a baseline using metrics that match your task. Experiments are tracked with MLflow so results are reproducible. The finished model is deployed as an API or on your own infrastructure, with documentation of how it was trained and where it falls short.

Good training data matters more than the training itself. If you don't have labeled examples yet, start with AI Datasets & Annotation.

Training teams

AI tools are only useful if people know when to trust them, when to check them, and when not to use them at all. Our workshops are built around your team's real tasks rather than generic demos:

• Practical use — prompting techniques, structuring work with AI assistants, and building repeatable workflows for common tasks.
• Judgment — what language models are good and bad at, how they produce confident mistakes, and how to verify output.
• Safe use — what information must never be pasted into external tools, how to handle personal and confidential data, and how to set sensible internal guidelines.
• Formats — introductory sessions for whole teams, hands-on workshops for specific roles, and follow-up sessions once people have tried things in their own work.

Team training is led by our co-founder Shogher Ghazaryan, who taught IT for more than a decade, trained teachers and directed a coding academy, alongside her engineering and analytics work. Meet the team.

If your organization operates in the EU, the AI Act expects organizations that use AI systems to take measures to ensure sufficient AI literacy among their staff. Structured training can be part of how you meet that; it is not a substitute for legal advice on your obligations.

What's included

• Feasibility check: prompting, retrieval or fine-tuning?
• Training, validation and test data preparation
• Fine-tuning of open language models
• Classical machine learning models for classification and scoring
• Evaluation against baselines with task-specific metrics
• Experiment tracking and model versioning with MLflow
• Deployment as an API or on your own infrastructure
• Hands-on AI workshops for non-technical teams
• Practical prompting and AI workflow training
• Safe-use guidelines: privacy, verification and limitations

How it works

1. Define the task, the success metric and the baseline to beat.
2. Prepare data and run a small, measured experiment.
3. Train, evaluate and compare — and only proceed if it is clearly better.
4. Deploy and document the model, or train your team to use it.