Most AI retrieval is stochastic: the same question can fetch different context every time. ColdState takes the opposite path — the same query returns the same ranked results, every run, with a clear reason for each. No embeddings, no vector database, no per-query inference.
Embedding models and approximate nearest-neighbor search are probabilistic. Re-index, bump a model version, or just run it again, and your rankings can shift — which makes agent behavior impossible to reproduce.
Vector pipelines embed the query, search the index, and often rerank — each step burning compute. Your bill scales with traffic, not with value.
A cosine distance between two vectors is not an explanation. When retrieval is a black box, debugging a bad answer means guessing.
Identical query in, identical ranking out — byte for byte. Cache it, snapshot it, diff it in CI. No temperature, no drift between runs.
A token-level explain endpoint shows exactly which terms matched and how each contributed to the score. Relevance you can read, not infer.
Documents are indexed once and scored deterministically at query time. No embedding model, no vector database, no GPU in the hot path.
Reach it through a REST API or an MCP server that drops straight into Claude and other assistants — or export your index as a portable file and run it offline.
Give Claude and other assistants a retrieval layer that returns the same context for the same task — so agent runs are repeatable, not a roll of the dice.
Pin your retrieval output and diff it in CI. When results can't drift, a failing eval points at your prompt or model — never at a flaky search layer.
Every ranking is explainable token by token and reproducible after the fact, so you can show exactly why a document surfaced.
No embedding step and no per-query model inference means query cost stays flat as you grow — no GPU bill that scales with traffic.
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