Everyone's engineering context, we're predicting it. Introducing Papr memory API

Most AI systems today rely on vector search to find semantically similar information. This approach is powerful, but it has a critical blind spot: it finds fragments, not context. It can tell you that two pieces of text are about the same topic, but it can't tell you how they're connected or why they matter together.

To solve this, everyone is engineering context, trying to figure out what to put into context to get the best answer using RAG, agentic-search, hierarchy trees etc. At Papr we tested almost every option that exists. These methods work in simple use cases but not at scale. That's why MIT's report says 95% of AI pilots fail, and why we're seeing a thread around vectors not working.

Instead of humans engineering context, we've built a model to predict the right context. Our model ranks #1 on Stanford's STARK benchmark that measures retrieval in complex real-world queries (not useless needle in a haystack benchmark). It's also super fast because it's predicted in advanced, which is essential for a ton of use cases like voice conversations. Try it out on papr.ai, our open source chat app or use papr's memory APIs to create your own experiences with papr.

We've also developers a retrieval loss formula and show that Papr's memory APIs get better with more data. Not worse like other retrieval systems today. A similar pattern to LLMs - the more data the better.

Predicting context instead of just matching fragments is a total game-changer. Awesome to see the STARK benchmark results to back it up!