The governed knowledge layer
Your agents are confident.About the wrong thing.
Litmus is the governed knowledge layer underneath them: it turns the changes your systems already record into one joined source of truth, resolves what's missing or contested, and serves it - with its condition attached - to every agent you run, over MCP.
The demo
Watch it work.
Same question, asked twice: once to an agent working from what it can find on its own, once to the same agent with Litmus in the loop. Same model, same question - the only difference is what it knows.
Shareable link: thelitmus.ai/demo
The problem
It doesn't look wrong. It looks like an answer.
Every agent you run answers from your company's knowledge. Look at the layer it's consuming.
It's scattered.
Knowledge isn't in one place. It's split across the suite, the wiki, tickets, chat, billing, and code, and every agent stitches its own version from whatever it happens to reach. Ask the same thing twice and get two different answers, both confident.
It contradicts itself, and nothing says so.
Teams define the same term differently, documents outlive the systems they describe, and pieces go missing entirely. That's normal. What's missing is anything that marks it - so a contested definition gets served exactly like a settled one.
It's permissioned by the wrong thing.
Access controls protect a drive or a channel. What needs protecting is a fact, and a fact surfaces from many containers at once. The moment an agent aggregates across systems, container-level permissions stop describing who should see what.
It's always behind.
The work happens in tickets, code, and decisions, every day. The record of it updates only when someone remembers to write it down - so the layer agents read from is stale by construction, not by accident.
None of this is a model problem. It's the state of the knowledge underneath, and right now it lives in exactly one place: whoever's in the room when the question comes up.
The turn
This is a data engineering problem.
Data platforms already solved the same problem for structured data: raw sources become conformed structure, then one governed layer every consumer reads from - owners, definitions, lineage, freshness. That's what made AI usable on top of a database. Company knowledge needs the same treatment.
Every category optimizes the path from question to text. None owns the state of the knowledge itself.
How it works
Observe. Resolve. Serve.
- 01
Observe
Litmus ingests change from the systems your work already runs through - a document edited, a plan updated, a decision made. Everything that goes in is accountable authorship: an act a person put their name on, not a message read or a channel watched.
- 02
Resolve
Conflicts and gaps surface as named, owned issues, never hidden and never auto-cleaned. Authority wins, never volume - a genuine update replaces what it supersedes automatically, and only a real contradiction stays open, listed, until someone settles it.
- 03
Serve
Every agent that asks - over MCP, inside whatever it already runs in - gets the answer plus its condition: current, contested, or missing something it should have. Contested knowledge never goes out marked as settled.
Condition travels with the knowledge - so an agent that hits something contested says so, instead of guessing.
What it is not
No new tab to open.
Not an assistant.
No chat window of its own competing for attention. It answers inside Claude, ChatGPT, and Copilot - wherever your agents already run - over MCP.
Not a wiki.
Nobody authors documentation into it, including you. Knowledge is distilled from change, not written by hand.
Not another memory.
It's the shared layer every agent's own memory should defer to, not one more silo per agent.
Every agent you run finally knows the business - because it's reading from the same layer, not memorizing its own version of it.
Deployment and safety
Safe to say yes to.
Deploys into your cloud, not ours.
One click from the AWS, GCP, or Azure marketplace, into the account you already control. The data never leaves it.
Bring your own keys.
Cloud and LLM - that's the whole setup. No content ever phones home.
Nobody has to run it.
Unattended upgrades, self-healing, and a failure mode that degrades to agents falling back to raw sources - never someone logging in to fix it. Built for a team with no platform function, because that's who's buying it.
Priced as infrastructure.
A flat annual band by connected systems and consumption, not by headcount. It scales with the corpus and the agents reading from it, not the size of the team.
The data never leaves your cloud, and nobody has to run it.
See the condition of your own knowledge.
Connect a few read-only sources and see the issues already living in your own knowledge - within the hour.
Prefer email? yair@thelitmus.ai
We use your email only to set up the demo. No lists, no sharing.