K² model stack
Dense clustering maps semantic neighborhoods, sparse signals capture exactness, fusion blends those signals per query, and reranking enforces calibrated evidence quality. Because all four models tune together on common signals, they form a shared relevance view.
Representative domain queries and mined retrieval errors tune dense, sparse, fusion, and reranker models in one shared optimization cycle.
Recall, precision, latency, and cost land in a unified harness so teams can tune trade-offs together instead of guessing.
Ship the stack into your VPC or cloud with documented recipes, integration guides, and observability hooks.
Retrieval stack in motion
Parallel dense and sparse retrievers feed a learned fusion model that shifts between semantic breadth and lexical precision. The reranker then delivers the evidence decision and logs outcomes into the next training cycle.
Aligned dense, sparse, fusion, and reranker models can improve on fixed heuristics, with gains measured on domain evaluation sets.
Calibration and tighter ranking reduce prompt growth while preserving the evidence quality needed to answer.
Shared dashboards let you monitor recall, precision, and latency trade-offs in one place.
The result is a retrieval stack tuned to your domain and measured against the same evaluation criteria on every release.
Shared training loops keep dense, sparse, fusion, and reranker models pushing in the same direction.
Dense clustering maps semantic neighborhoods while sparse tokens capture exact salience.
The learned fusion model dynamically blends the two, producing a ranked shortlist tailored to each query.
Calibrated scores support thresholds for grounded answers or deferrals.