In practice
For an industrial engineering client, we built a document-understanding pipeline (OCR, computer vision, and a large language model) that turns valve datasheets into structured, cited records. Engineers upload a file or a folder; the system returns validated data and source snippets. That output feeds procurement and project tooling.
Add AI inside the product you already have
Most users will never visit a chatbot. They meet AI inside the apps they already use: smart drafting in a form, suggestions in a workflow, semantic search across content, or an agent in support.
We put foundation models (Claude, GPT, Gemini, open-source) and our RAG and agent infrastructure into your iOS, Android, and web products, with the polish and reliability of native features.
Common patterns we build
- Smart drafting and rewriting: emails, reports, product copy.
- Semantic search over your documentation, product catalogue, or knowledge base.
- Categorisation and routing: tickets, leads, content moderation.
- Voice and multimodal interfaces: speech-to-action, image understanding.
- Personalisation: recommendations grounded in real user behaviour.
Every integration ships with cost monitoring, fallback behaviour, and a way to evaluate quality over time.
