Evidence-driven retrieval and knowledge systems.
Cajun Data is an independent applied research and engineering studio. We design, build, and evaluate systems that help machine-learning models find, organize, and use external information, without hyperscale infrastructure.
Capable knowledge systems should not require data-center-scale infrastructure, and claims about their performance should be supported by reproducible evidence.
Search architectures and precision ordering, from lexical baselines to learned rerankers.
How information is chunked, embedded, and structured so systems can actually use it.
Turning messy documents and changing sources into clean, fresh, indexable data.
Constructing the context a model sees deliberately, instead of hoping for the best.
Outputs tied to inspectable sources, with the seams showing on purpose.
Benchmarks, failure diagnosis, and instrumentation for complete system behavior.
Architectures that run on workstations, modest servers, and ordinary cloud services.
Reference implementations and templates published so results can be checked.
What the studio is investigating right now. Questions move to the Lab as they produce results.