CoreCast AI started with a recurring problem. It grew into the infrastructure layer that every AI team needs but nobody had built properly.
We built CoreCast after watching our own agents forget everything five minutes into every conversation. After the third rebuild of the same memory layer from scratch, we realized every AI team was solving the same problem independently and badly.
Marcus had spent years on agent infrastructure at Scale AI before moving to Anthropic's agent team. The pattern was always the same: brilliant engineers burning weeks on retrieval logic, context window management, and session state that should have been solved infrastructure. The agents themselves were getting smarter - the memory around them wasn't.
CoreCast AI incorporated in 2023. The first version of the SDK shipped three months later. The first customer - a YC-backed agent startup - integrated in a single afternoon and had working persistent memory before the end of the day. They told five other founders. Those five told ten more.
Today we store over 10 million indexed agent conversations and serve recall queries in under 50 milliseconds. We're a team of builders who believe memory is the missing infrastructure layer for the AI era - and we're here to provide it.
Agents can't wait. A memory layer that adds 500ms to every tool call is worse than no memory at all. We've obsessed over recall speed since day one - every architectural decision we make starts with latency.
Every user deserves an agent that remembers them. An agent that forgets who you are after every session isn't intelligent - it's just a fancy search box. We believe persistent, personalized context should be a baseline expectation.
Memory is personal data. We built row-level isolation, configurable retention, and GDPR compliance into the core architecture - not as afterthoughts. Your users' memories belong to your users.
The shift from stateless chatbots to persistent AI agents is still in its early stages. The infrastructure to support that shift - reliable, fast, private memory - is exactly what we're building.
Our roadmap includes deeper framework integrations, multi-modal memory, and enterprise-grade memory governance tools. We're not trying to be a general AI platform - we're trying to be the best possible memory layer for AI builders worldwide.
Meet the Team