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.
Production queries and mined mistakes 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 outperform heuristic approaches on domain-grounded queries.
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 state-of-the-art retrieval performance, precision-tuned to your domain and measurable 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.
Our reranker delivers the expert judgment, producing calibrated scores for grounded answers or safe deferrals.