A few months ago, we launched Papr — a predictive memory layer for AI agents. It helps agents remember conversations, documents, and context over time, so they don’t start from scratch on every interaction. Instead of just storing information, Papr learns the connections between memories and surfaces the right context in real time, exactly when it’s needed.

Today, we’re building on that foundation. We’re introducing Papr Context Intelligence — the ability for agents to not only remember context, but to make sense of it: to reason over information, generate insights, and understand what changed and why.

Here’s a simple example of what that means in practice.

Imagine an AI assistant helping a customer support team. Before context intelligence, the assistant can retrieve past tickets and related conversations. If you ask, “Why is this customer frustrated again?”, it might surface previous messages or similar issues — leaving a human to piece together what actually happened.

With Papr Context Intelligence, the assistant understands the situation. It can explain that the customer experienced the same login issue last month, that the original fix didn’t fully resolve it, and that a recent change reintroduced the problem. It can also tell you that 37 other customers are currently reporting the same issue, that reports spiked after the latest release, and that most affected users are on the mobile app.

Instead of just showing history, the agent explains what changed, why it’s happening, and how widespread the issue is — helping teams respond faster and decide what to prioritize.