Train your team on AI, and train AI on your data
Two tracks. Hands-on sessions that get your people using and building with AI safely, and fine-tuning that makes models accurate on your own cases, terms and documents.
Signs you need AI training
AI use without rules
Staff use public tools with company data because nobody showed them an approved way
Generic models miss your context
Off-the-shelf answers ignore your terms, categories and policies
Leaders unsure where to start
Plenty of interest in AI, but no shared understanding of what it can do here
Skills for your people, accuracy for your models
Team training uses your own tools and examples. Model training is measured on your own test cases.
| Track | Format | Outcome |
|---|---|---|
| AI for leaders | Half-day session | Use-case shortlist |
| Prompting and agents | Hands-on workshops | Approved ways of working |
| Model fine-tuning | Project | Higher accuracy on your tasks |
- AI literacy for leadersWhat AI can and can't do, where it pays off, and the risks
- Hands-on team workshopsPrompting, agents and AI tools your company has approved
- Build-your-own sessionsEach team builds one useful workflow on its own data
- Model fine-tuningOpen-source or hosted models trained on your labelled examples
- EvaluationBefore and after accuracy on a test set built from your cases
How a training engagement runs
Assess
Skills, tools in use and the tasks that matter
Design
Programme or fine-tuning plan
Deliver
Workshops, or data preparation and training
Measure
Adoption, or accuracy on your test set
Embed
Guides, champions and retraining plans
We build on the stack you already run
- Claude
- OpenAI GPT
- Llama
- Mistral
- Hugging Face
- PyTorch
- Databricks
Explore the rest of our AI work
See it in practice
Targeting portfolio growth for financial advisers
Machine learning segmentation on sensitive client data, giving advisers targeted suggestions for portfolio expansion
What buyers ask before starting
Who is team training for?
Leaders, operations and technical staff.
When is fine-tuning worth it?
When prompting alone isn't accurate enough. We test that first.
Does our data leave our environment?
Only on terms you approve, in your cloud or with approved providers.