The Rediscovery Tax is the cost enterprises pay when AI agents re-process answers they already have, billing at full frontier LLM rates for queries their systems have already resolved. It is caused by the stateless nature of large language models, which retain no memory between sessions. Excipio eliminates the Rediscovery Tax by intercepting repeated queries at the gateway and serving cached answers in under 10ms at near-zero cost.
The Rediscovery Tax is the cost enterprises pay when AI agents re-process answers they already have. Because LLMs are stateless, agents bill at full frontier rates for every query, including those already answered in prior sessions. Excipio eliminates this tax through semantic caching.
Agentic systems waste up to 85% of compute rediscovering context they should already know. At $15 per million tokens, repeated queries at frontier rates compound rapidly. A 42% semantic cache hit rate eliminates 42% of that spend on day one with no code changes.
Excipio sits between your AI agents and the LLMs they call. Every query is vectorized locally and matched against a per-agent cache. Semantically similar prior answers are served in under 10ms at $0.002 per million tokens. Zero tokens are spent. Zero data leaves the perimeter.
Large language models retain no memory between sessions. Every agent call starts from scratch. In multi-agent workflows where dozens of agents fire hundreds of LLM calls per task, the same effective queries are answered repeatedly. Each repetition is billed at full frontier cost.
Related but distinct. Context window waste is the cost of transmitting redundant conversation history within a session. The Rediscovery Tax is the cost of re-answering questions across sessions, because the model has no persistent memory. Excipio addresses the cross-session problem that context windows cannot.