Capability
Intelligent Search & RAG
Answers grounded in your data, with the source attached
Search that understands what someone meant, over inventory or documents that were never organised for searching — and answers that carry the passage they came from, so a reader can check rather than trust.
What we build
- Search engines over inventory, catalogues and listings
- Enterprise knowledge systems across mixed document formats
- Retrieval pipelines that resolve loose phrasing into real constraints
- Grounded assistants that answer only from what was retrieved
What is hard about it
Retrieval is the product; generation is the last step
Most of the engineering in a working RAG system is in what gets retrieved and why. A model given the wrong three passages will write a confident, fluent, wrong answer from them, and no amount of prompt work fixes a retrieval problem.
Refusing is a feature
A travel assistant that invents a villa is worse than no assistant, and a knowledge assistant that fills a gap with something plausible is worse than one that says it does not know. The boundary between what the sources support and what the model would like to add has to be enforced and visible.
Permissions belong at retrieval, not at display
If access is checked when the answer is rendered, the model has already read the document — and a summary leaks what a redaction would have caught. Filtering at retrieval is the only version that holds.
Proven in