AI that solves a real problem — not a demo

Use cases that work today

• Assistants on your own content — answer customer or employee questions from your documentation, with sources shown
• Document processing — extract data from invoices, forms, contracts or emails into structured records
• Classification and routing — sort support tickets, leads or messages automatically
• Search that understands meaning across your products or knowledge base
• Prediction models — demand, churn or risk scoring from your historical data
• Content assistance — drafts and summaries that a human reviews before publishing

How we make AI reliable

1. Define the task and what "good" looks like, with a test set of real examples.
2. Choose the simplest approach that meets the bar — sometimes rules or classic ML beat a large model.
3. Measure accuracy, latency and cost before launch, and keep monitoring after.
4. Add guardrails: retrieval from trusted sources, input/output checks and human review for important decisions.

Data protection first

We minimise what is sent to external models, prefer providers with EU data processing options and clear data-use terms, and document the data flow for your GDPR records. Where needed, models can run on infrastructure you control.