Limma.ai provides a verification runtime for autonomous AI systems, ensuring that AI agents can only act on information they can prove. The platform intercepts proposed actions and validates them against cited evidence before execution, returning verdicts of proceed, hold, or refuse with a full proof chain. It enforces source verification, identity rules, cross-source pairing, and independent computation at the infrastructure layer, below the agent's code.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous AI agents often act on unverified or conflicting information, leading to costly errors such as incorrect payments, unauthorized transactions, or actions based on hallucinated data. Without a formal verification layer between model reasoning and tool execution, there is no reliable mechanism to ensure that consequential actions are grounded in provable facts.
Solution
Limma.ai provides a verification runtime that sits between an AI model and its tool calls, intercepting every proposed action and its supporting facts before execution. The platform runs four mandatory passes—source verification, identity rules, cross-source pairing, and independent computation—to assess whether the action can be grounded in cited evidence. Each run returns a verdict of proceed, hold, or refuse, accompanied by a complete proof chain that traces every fact back to its original source, page, table, or cell. This infrastructure-level enforcement ensures that no agent can bypass verification, regardless of its code or discretion. The verdict and its evidence travel with the output artifact, providing a transparent audit trail for every consequential action.
Target Audience
Primary customers are engineering and operations teams deploying autonomous AI agents in enterprise environments where consequential actions—such as financial transactions, contract execution, or system changes—require provable accuracy and auditability.
Features
- Four-pass verification pipeline: source verification, identity rules, cross-source pairing, and independent computation
- Evidence chain generation that cites specific pages, tables, rows, and cells for every fact used in a decision
- Verdict system returning proceed, hold, or refuse with a full proof chain attached to each action
- Infrastructure-layer enforcement that operates below the agent's code, making verification non-bypassable
- Support for structured data sources including PDFs, CSV files, contracts, and approval matrices
- Automated routing of refused actions to designated human leads with queue identifiers for follow-up