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Two agents give a customer different answers. Legal asks what an answer was based on. An agent touches data it shouldn't have. The pilot won't roll out to the next team.
What you've probably triedPointing agents at the shared drive, longer prompts, Claude or ChatGPT Projects, a memory plug-in, maybe a retrieval pipeline someone built in a sprint.
- “Why did the agent use an old document?”
- “How do I stop an agent from reading certain folders?”
- “How do we keep several agents consistent?”
- “Our pilot works. How do we scale it across teams?”
Most teams climb this ladder in order.
- Step 1
The first agent
It works. Its knowledge lives in one person's prompts and files.
Works: Nothing breaks yet.
- Step 2
The fifth agent
Agents start to disagree, and nobody knows which document is current.
Starts to strain: Inconsistent answers, stale policy.
- Step 3
The second team
Sharing means copying, and permissions start to matter.
Starts to strain: The pilot won't scale. Restricted data leaks.
- Step 4
A second model or runtime
Moving to the cloud, a web app or another model leaves context behind.
Breaks: Laptop to production, lock-in, cost.
- Step 5
The client-facing agent
A wrong answer is now a liability. You have to show what the agent used, and never mix one client's data with another's.
Breaks: Audit, access, trust.
The proof that matters to you.
A demo on documents like yours
What the agent saw, and what it couldn't see.
Who read what, and when
Every read is recorded: which person or API key, which document, and when. Every approved version is kept.
Read more →It works with the tools you run
Claude, Cursor, any MCP client and your own agents.
Read more →A price you can approve
Start free. No committee needed to try it.
Read more →Start where you are.
Start free with the AI tools you already use, or book a demo on documents like yours.