CEF provides an event‑driven orchestration platform that runs multi‑agent AI inference at sub‑second latency on edge, cloud, or hybrid environments. It integrates model evaluation with immutable audit logs, adaptive memory, and sovereign data vaults to ensure data lineage, encryption, and compliance, while offering a replay sandbox and CI/CD integration for safe deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises that require real-time, edge‑native execution of multi‑agent AI systems face fragmented tooling: legacy batch pipelines, opaque model inference, and limited data lineage hinder operational reliability and regulatory compliance. Without unified orchestration and sovereign data controls, deploying robotics, computer‑vision, or fleet‑management agents at scale becomes costly and error‑prone.
Solution
CEF delivers a single platform that combines orchestration, data management, and compute for continuous, event‑driven inference across distributed agents. The runtime executes millisecond‑level decisions on‑prem, in VPCs, public clouds, or hybrid clusters, preserving low latency for edge workloads. Integrated model evaluation generates auditable decision logs, while adaptive memory maintains long‑term context across agent interactions. Sovereign data vaults enforce end‑to‑end encryption and role‑based access, guaranteeing full data ownership and compliance. Built‑in data lineage and replay sandbox enable safe testing and rollback of new logic on real production streams. The platform exposes APIs for seamless integration with existing CI/CD pipelines and monitoring stacks, allowing organizations to ship, version, and monitor multi‑agent AI as software.
Target Audience
Primary customers are enterprises and product teams building production‑grade multi‑agent AI for robotics, computer‑vision surveillance, autonomous fleet management, and interactive gaming platforms. The platform also serves AI developers who need auditable, edge‑optimized infrastructure for real‑time decision making.
Features
- Event‑driven orchestration engine that schedules and routes inference tasks across heterogeneous edge nodes in real time
- Transparent model evaluation layer with immutable audit logs and per‑run provenance tracking
- Unified adaptive memory store that aggregates context from all agents, supporting long‑term reasoning and stateful workflows
- Sovereign data vaults with encrypted data pipelines, RBAC policies, and full lineage for regulatory‑grade data sovereignty
- Replay sandbox environment for safe validation of updated agent logic against historical data streams
- Sub‑second inference latency on edge hardware, optimized for video and sensor streams in robotics and fleet scenarios
- Deployment‑agnostic runtime supporting on‑prem, VPC, public cloud, and hybrid topologies via containerized services
- Cost and compute transparency dashboards that attribute resource usage to individual agents and model versions