AI where it pays off
Generative AI is useful when it fits into an existing process and its contribution can be measured. We start from your repetitive tasks and your data, not from the technology.
Common use cases
- E-commerce: product descriptions written from supplier data, translations, search that understands customer intent.
- Customer service: an assistant that answers from your documentation and cites its sources, ticket triage and suggested replies.
- Back office: data extraction from PDF invoices, purchase orders or contracts, automatic classification.
- Business tools: agents that chain actions in your software through their APIs, under human supervision.
A three-step method
- Prototype in 2 weeks on a sample of your real data.
- Measurement: an evaluation set built with you, a success rate and a cost per request, before deciding whether to go to production.
- Production rollout: guardrails, logging, quotas, fallback to a human, ongoing quality monitoring.
Privacy and compliance
Data minimised and pseudonymised before sending, providers whose contracts exclude training on your data, data processing agreements (DPAs) in place, European hosting where required. The AI Act’s transparency obligations are built in from the design stage.
This website is an example: describe your project in the quote form, and your needs are pre-analysed in real time by a Claude model, then reviewed by a developer.