Dense + sparse retrievers
A strong answer often needs both meaning and keywords. Dense encoders capture semantic context—synonyms, paraphrases, and domain jargon—while sparse heads retain lexical cues such as ticket IDs and product codes. Fine-tuning them on the same corpus gives both retrievers a shared definition of relevance while preserving complementary signals.
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 documented telemetry patterns.
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 representative hard negatives keeps both encoders aligned to the same domain language 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.