Why RAG Fails (And What We're Building Instead)

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Everyone's been chasing this dream of "just throw documents into a vector database and the AI will figure it out." It's fundamentally broken.

Here's why.

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The Compression Problem

RAG takes structured information and destroys it. A Salesforce contact has fields, relationships, history. RAG compresses that into a blob of text, then compresses that into a vector. Every step loses information.

When you retrieve, you're hoping that cosine similarity between vectors somehow captures "relevance." But relevance is contextual. "John from Acme" means nothing unless you know John is the VP of Engineering who you've been trying to reach for 3 months, who just posted about switching from Competitor X, and whose company raised Series B last week.

RAG can't know that. It treats all information as equal. But some facts are more important, more recent, more trustworthy than others.

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What We Build Instead

We designed a system where data enters structured and stays structured.

When someone sends a message in our internal Comms tool, we don't embed it. We parse it: Who sent it? Who's mentioned? What entities are referenced? What's the thread context? That all goes into the graph as first-class relationships.

When an agent needs context about a prospect, it doesn't do a semantic search and hope. It runs a graph query: give me this person, their company, their recent activity, signals about them, our previous interactions, and what worked with similar prospects.

That's deterministic. That's complete. That's not lossy.

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The Real Difference

This matters because agents need to actually understand things to do useful work.

Right now, most AI agents are stateless. They get a prompt, do a thing, forget everything. Even with RAG, they're reconstructing context every time from incomplete fragments.

Our agents have true memory. Not "here's a summary of what happened" memory. Actual structured memory where they can query: "What did I do last time I reached out to someone at this company? What was the outcome? What feedback did I get?"

The decision traces mean every action an agent takes becomes training data for itself. The agent literally sees "last time I did X in situation Y, I got a quality score of 4 and the human said Z." That's how humans learn. That's how these agents will learn.

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The Context Graph Solves Fragmentation

Look at what's happening in the market right now. Every company building agents has the same problem: agents are siloed. Your inbound SDR agent doesn't know what your outbound SDR agent knows. Your customer support agent doesn't know what your sales agent knows.

We're building the shared brain.

Our Research Agent discovers that Acme Corp raised Series B. That fact goes into the context store attached to the Acme entity. Now our Outbound Agent, when it's crafting a message to someone at Acme, automatically sees that signal. And our Inbound Agent, when deciding what content to post, knows which topics resonate with companies in our pipeline.

They're not siloed. They're reading and writing to the same brain.

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Enterprise Actually Cares About This

This matters for enterprise for two reasons.

First, auditability. When a customer asks "why did your AI send this message to my prospect?", you can show them the exact decision trace. Here's what the agent saw. Here's how it reasoned. Here's why it chose this action. Here's what quality score it got.

That's not possible with RAG-based systems where the AI is essentially a black box that retrieved some chunks and hallucinated an output.

Second, multi-tenancy. Because everything goes through the ontology, tenant isolation is just row-level security. Every query automatically filters by tenant. You never accidentally leak data between customers because it's enforced at the database level, not the application level.

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