Dense + sparse retrievers
A great answer needs both meaning and keywords. Our dense encoders grasp semantic context—synonyms, paraphrases, and domain jargon—while sparse heads lock onto lexical cues like ticket IDs and product codes. By fine-tuning them on the same corpus, they learn a shared definition of relevance from complementary viewpoints so no crucial signal is missed.
Fine-tuned on docs, tickets, chats, and runbooks, dense models capture synonyms, paraphrases, and jargon that matter to your team.
Sparse retrievers stay sharp on IDs, SKUs, error codes, and policy names, giving you deterministic hooks when you need them.
Deploy dense-only, sparse-only, or hybrid blends with open weights, ANN recipes, and telemetry wiring ready on day one.
Unified training means both models respect the same evaluation metrics and telemetry budgets.
Dense encoders grasp synonyms, paraphrases, and domain jargon while sparse models anchor to IDs, error codes, and feature names.
Joint fine-tuning on production hard negatives keeps both encoders calibrated to your latest traffic and edge cases.
Plug into existing vector stores and search clusters; we hand over open weights, ANN recipes, and batching strategies.
Run dense-only, sparse-only, or hybrid blends. Deploy via hosted endpoints or ship the open weights into your VPC.