Search 48.4M knowledge entries or bring your own data. Tokenized with 2.8M whole words. No LLM embeddings. No inference costs.
// 1. Create an index — no embedding pipeline
await fetch("https://cold-api.coldstate.ai/v1/indexes", {
method: "POST",
headers: {
"Authorization": "Bearer cs_live_...",
"Content-Type": "application/json"
},
body: JSON.stringify({
name: "acme-docs",
domain_preset: "general",
documents: [
{ id: "doc_001", content: "Parental leave policy..." },
{ id: "doc_002", content: "Remote work guidelines..." }
]
})
});
// 2. Search — deterministic, Ψ-scored
const res = await fetch(
"https://cold-api.coldstate.ai/v1/indexes/idx_.../search",
{
method: "POST",
headers: { "Authorization": "Bearer cs_live_..." },
body: JSON.stringify({
query: "parental leave policy for remote employees",
limit: 5
})
}
).then(r => r.json());
// res.results[0].state: "CRYSTALLINE"
// res.results[0].score["Ψ"]: 0.94
// res.diagnostics.execution_time_ms: 847This is what we call Deterministic Knowledge Infrastructure. See why deterministic retrieval matters, how it stacks up in RAG alternatives, explained, or go straight to the API reference and pricing.
A real, read-only index running on the live API. Run a query, expand any result to see the per-term scoring, then verify the same query returns a byte-identical ranking every time.
A deterministic search API returns the same ranked results for the same query, every time. ColdState computes relevance from whole-word tokenization (a 2.8M-word vocabulary) and transparent scoring instead of LLM embeddings — so rankings are reproducible, explainable, and cacheable. There is no model inference, no temperature, and no drift between runs.
Vector search embeds your query with a neural model and finds approximate nearest neighbors — results can shift between model versions, and every query pays an inference cost. ColdState indexes documents once and scores them deterministically at query time: no embedding step, no approximate search, no GPU. The explain endpoint shows exactly why a document ranked where it did, token by token.
Yes. ColdState hosts a Model Context Protocol (MCP) server with 16 read-only tools — search the global knowledge base, fetch an entry verbatim, cite it, verify it later, find related entries, and search, browse, and explain your own indexes — so Claude and other MCP-compatible AI assistants can use ColdState directly. It runs over HTTP with the same API key, so there is nothing to install.
Agents built on stochastic retrieval are hard to debug: the same prompt can fetch different context on every run. Deterministic retrieval makes agent behavior reproducible — evals are stable, failures replay exactly, and every ranking decision can be audited after the fact.
Alongside your own indexes, ColdState exposes a curated knowledge base of 48.4 million entries across 35 domains — science, medicine, technology, law, history, and more — searchable through the same deterministic API and MCP tools.
Yes. IaaS (Indexing as a Service) mode builds your index server-side and delivers it as a portable SQLite file you can query offline — no ongoing hosting relationship. Hosted mode keeps the index on ColdState with encrypted-at-rest storage.
Search our knowledge base or bring your own data. Get your API key and start in under a minute.
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