About Adaptive Recall

Adaptive Recall is an adaptive memory system for AI applications. Patent pending, it learns which retrieval strategies work best for your data, scores results using cognitive science models, builds a knowledge graph automatically, and validates every parameter change against real query history before adopting it. The result is AI memory that gets measurably better the more you use it.

Why We Built It

Most AI memory is static: store text, embed it, hope the right piece comes back. Real applications need more. Support bots, agents, and assistants all ask different kinds of questions against different kinds of data, and the retrieval approach that works for one fails for another. Adaptive Recall treats retrieval as something to be learned, measured, and improved continuously, the same way people refine their own memory through use.

The Product

Adaptive Recall runs as a hosted service you connect to in minutes. Explore the features, see pricing, read the documentation, or create an account and start storing memories today. Developers can also review the open repository on GitHub.

The Research Library

We publish in-depth guides on the engineering around AI memory, including RAG pipelines, LLM fine-tuning, agentic AI, prompt engineering, AI guardrails, and LLM caching. The articles library collects all of it in one place.

Who Builds It

Adaptive Recall is built by AI Apps API, a company building AI and automation software across a network of sites and open-source projects. We use Adaptive Recall inside our own AI systems every day.

Get In Touch

Questions about the product, the docs, or partnership? Reach us any time through the contact page.