MIDAS provides an operating system that lets enterprises design, deploy, and manage AI agents integrated with existing ERP, CRM, and database systems. It enforces organization‑level isolation, sovereign context, and explicit governance, delivering full traceability and compliance for each AI‑driven decision while allowing specialized domain agents to operate under centralized policies.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to integrate large language model (LLM) agents with existing ERP, CRM, and database systems while maintaining strict governance, data isolation, and auditability. Without a unified framework, AI deployments become fragmented, risky, and difficult to control across organizational boundaries.
Solution
MIDAS delivers an operating system that orchestrates AI agents as first‑class services within an enterprise’s IT landscape. The platform connects agents to legacy systems through a dedicated MIDAS layer, enabling domain‑specific tasks such as customer inquiries, billing updates, or inventory checks. Governance is enforced by configurable policies that define language tone, permissible actions, and compliance rules, while a sovereign context per organization guarantees data isolation. Every decision made by an agent is logged with full traceability, linking the executing agent, applied context, and governing rule for easy audit. The system’s control plane manages deployment, scaling, and monitoring of agents, allowing organizations to design, launch, and oversee AI workloads under a single, auditable framework.
Target Audience
Primary customers are mid‑to‑large enterprises that need to embed AI assistants into their core business systems while retaining full regulatory and operational control.
Features
- Integration adapters for ERP, CRM, and relational databases that expose existing business functions to AI agents
- Sovereign context management that isolates operational data and model state per organization
- Policy engine that governs language, action limits, and compliance constraints for each agent
- End‑to‑end audit logs capturing agent identity, invoked context, applied rule, and outcome for every transaction
- Centralized orchestration layer that handles agent lifecycle, scaling, and health monitoring
- Role‑based access controls and multi‑tenant isolation to prevent cross‑organization data leakage