Insights
Articles focused on retrieval architecture, document intelligence, and the engineering demands of enterprise AI systems.
Most RAG demos collapse under real document volumes. The hard part isn't the model — it's the ingestion pipeline, index consistency, and retrieval latency at scale.
LLMs give us a general-purpose intelligence layer. The product is everything required to turn that intelligence into dependable work.
Citing sources isn't a UI feature — it's an architecture constraint that shapes chunking, retrieval, and context assembly from the ground up.
We indexed 2M+ résumés and learned that the hard part isn't embeddings — it's relevance calibration, recruiter trust, and keeping the index fresh at scale.
Products
The thinking behind these articles powers Disvania Legal and Disvania Knowledge.
