About WorldFlow AI

WorldFlow AI is the enterprise memory layer for AI. We provide three core capabilities: semantic caching at the API gateway to eliminate redundant LLM calls, KV-cache inference acceleration at the GPU for 2-12x faster inference, and long-term agentic memory that persists across sessions. Together, these give your AI applications memory -- making them faster, cheaper, and smarter.

Unlike traditional exact-match caches, semantic caching uses embedding models to match queries by meaning, not just identical text. When a new request comes in, we compute its semantic embedding and compare it against previously cached responses. If a similar enough query has been seen before, we return the cached result instantly -- even if the wording is completely different. This dramatically increases cache hit rates compared to exact-match approaches.

WorldFlow AI works with any OpenAI-compatible API, which includes OpenAI, Azure OpenAI, Anthropic, Google Gemini, Mistral, and any self-hosted model served via vLLM, TGI, or similar inference servers. Our gateway sits between your application and the LLM provider, so you get caching and acceleration regardless of which model you use.

Absolutely. All data is encrypted end-to-end with TLS 1.3 in transit and AES-256 at rest. We enforce strict multi-tenancy isolation so no data is shared between customers. SOC 2 Type II certification is in progress, and we offer a zero data retention option for the most sensitive workloads. You can also deploy WorldFlow AI in your own VPC for complete control.

KV-cache acceleration reuses the GPU attention states (key-value pairs) computed during previous inference requests. When a new request shares context with a previous one -- such as the same system prompt, documents, or conversation history -- we inject the pre-computed KV-cache directly into the GPU, skipping the expensive prefill computation entirely. This delivers 2-12x faster time-to-first-token depending on context length, with virtually no quality degradation.