K² retrieval family
Dense clustering maps semantic neighborhoods, sparse tokens capture salience, fusion blending dynamically adjusts the mix, and the reranker learns which evidence is truly useful. Because all four models are tuned on the same data and hard negatives, they develop a unified concept of relevance that keeps compound gains flowing.
Production queries and mined mistakes tune dense, sparse, fusion, and reranker models inside the same 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 ANN recipes, integration guides, and observability hooks.
Retrieval stack in motion
Parallel dense and sparse retrievers feed a learned fusion model that understands when to trust semantic breadth or lexical precision. The reranker then delivers the expert verdict—grounding your generator while logging every outcome for the next training cycle.
Aligned dense, sparse, fusion, and reranker models consistently beat single-retriever or heuristic hybrids on domain benchmarks.
Lean, calibrated retrieval reduces prompt bloat so downstream generators stay fast and inexpensive.
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.