Find and Reuse What Your LLM Traffic Already Answered

Govern access before spend happens

Exact-match caching only catches requests that are byte-for-byte identical, but a large share of repeat traffic is semantically identical: the same question asked in different words, the same classification with different phrasing. Without semantic tooling, that traffic re-runs the full model every time, and finding a specific past request, tool call, or guardrail violation for debugging means grepping logs by hand.

• Byte-identical caching misses the larger population of near-duplicate requests
• No way to search prompts, tool calls, or violations by meaning
• Wrong-answer risk from serving a semantically 'close enough' response with no safety gate
• No way to tell if a cached embedding is backed by a real model or a placeholder

One policy layer across model usage

Cloptima indexes traces, prompts, tools, and policy violations into a tenant-partitioned vector store, so you can search LLM activity by meaning across your whole estate. Semantic cache eligibility is a separate, stricter gate on top of that index: a request only qualifies for a cached hit after passing namespace isolation, route and model-family matching, tool-schema matching, source-freshness checks, and content-class checks — and, for enforce mode, an eval-score threshold and, where configured, human approval. Enforce mode is hard-blocked from activating on any collection still backed by placeholder embeddings.

• Tenant-partitioned semantic search across traces, prompts, tools, and violations
• Semantic cache observe mode: log would-be matches and similarity scores with zero behavior change
• Semantic cache enforce mode: serve only after namespace, route, model-family, tool-schema, freshness, and content-class checks pass
• Eval-score gate and optional human-approval gate before enforce-mode serving
• Hard gate against enforcing on stub or placeholder embeddings — real embeddings are required before enforce mode can activate

Start with one app, then expand

Start with semantic search alone — useful immediately for finding related traces, debugging a guardrail violation, or auditing a class of prompts. Layer in semantic cache observe mode once real embeddings are indexed, and reserve enforce mode for a narrow, eval-scored class of requests.

Built for private production AI

Search and cache candidate lookups run entirely outside the synchronous provider request path — a candidate lookup either returns fast or the request proceeds to the provider with no added latency floor. The candidate endpoint never returns response bodies, only pointers (cache key hash and policy id), so gateway-core replays a hit through the same safety path as the exact-cache Redis layer rather than trusting a vector-store result directly.