AI cannot act on what it cannot prove.
The verification runtime for autonomous AI.
Heterogeneous sources, specialised extraction, one verification step.
Documents, spreadsheets, databases, APIs, emails. Each routes to the extraction agent that knows how to read it. Every output passes through the same verification node before it lands in the substrate. The agents coordinate. The verification step is singular.
Verified knowledge for consequential AI.
Most agent frameworks let the model reason directly into a tool call. Limma puts a verification step between the model and the action. Every consequential call posts its proposed action and supporting facts. The verdict comes back proceed, hold, or refuse, with the full proof chain attached.
Every fact comes from a source with a citation back to the page, table, or cell.
Source verification, identity rules, cross-source pairing, and independent computation. Four passes, every run.
Outputs carry the chain that produced them. The verdict and its evidence travel with the artifact.
Three reads on the architecture.
Each one stands alone. Pick the angle that maps to the question you came in with.
Three structural touch points. None of them bypassable.
Discretion is not a security model. Anything the agent can skip will be skipped on the call that matters. The wrap is enforced at the infrastructure layer, below the agent’s code, at every point where the agent touches data, calls a model, or commits an action.
Every source enters Limma first. The agent queries the workspace and receives typed facts with citations and verification status attached, never raw files or rows. Enforced at the data layer.
The customer points the LLM base URL at Limma. Every model call routes through the proxy and is re-checked against the workspace before it returns. Enforced at the network layer, below agent code.
Every consequential action produces a signed contract carrying inputs, workspace state, verdict, and a cryptographic anchor. Downstream consumers require the contract to accept the action. Enforced at the receiver.
Trust a structure, not a model.
Every other framework asks the user to trust the model. Limma asks the user to trust a structure. If you are building agents that take consequential action, this is the layer that makes them safe to ship.