What Is AttentionDB?

If you're building AI-enabled tools, you've probably run into this: you ask your system a question, and it spits out five paragraphs that seem vaguely related, but none actually answer what you asked.

This isn't an accident. It's the result of a memory stack that was never built to reason.

Vector Search Is Not Memory

The vast majority of LLM "memory" solutions today are powered by vector databases. They measure semantic similarity — cosine distance between dense representations of chunks.

This is great if you're searching FAQs. But if you're navigating a codebase, tracing a logic chain, or auditing policy, it's almost useless.

You don't need something that looks similar. You need the right thing. You need structure. You need salience.

AttentionDB: Built on Relevance, Not Just Proximity

AttentionDB is the engine that powers Attixa. Instead of matching fuzzy vectors, it builds salience graphs: structured representations of your data that prioritize what matters, not just what sounds similar.

• Code functions are connected by call graphs, not keywords
• Policy sections are linked by cross-references, not cosine scores
• Logs are contextualized by sequence and hierarchy

This means:

• Fewer false positives
• Results that can be explained
• Queries that actually work for agents and AI-native tools

Salience Isn't Optional Anymore

We're leaving the prototype era of AI. We're now building systems that live in production, that touch money, users, critical infrastructure.

And those systems need memory that doesn't guess.

They need memory that focuses.

That's why we built AttentionDB.