Kanvaz.ai provides an autonomous decision layer for global supply chains, compressing cross-department coordination from 72 hours to under four seconds. The platform translates brownfield telemetry into validated actions, with AI agents collaborating across supply chain and finance functions to optimize EBITDA impact. Its cognitive clock orchestrates multi-agent consensus, from anomaly detection to ERP payload execution, while a use-case scout helps identify high-impact opportunities.
Funding
Funding not disclosed
Founders
Product
Problem
Global supply chains rely on fragmented, manual coordination across departments, creating 72-hour delays in exception handling and decision-making. This latency erodes EBITDA by 3–5% as teams struggle to reconcile inventory, logistics, and financial trade-offs in real time.
Solution
Kanvaz.ai delivers an autonomous decision operating system that collapses multi-department coordination into seconds. The platform ingests brownfield telemetry, detects anomalies, and orchestrates specialized AI agents—such as supply chain and finance agents—that evaluate alternatives, reach consensus, and lock validated actions. Every decision is explainable and human-validated, with the final payload formatted and broadcast directly to ERP systems like SAP. The system is ERP-agnostic and designed to recover deterministic EBITDA impact through continuous, autonomous execution.
Target Audience
Primary customers are global supply chain operators, logistics leaders, and finance executives at enterprises with complex, multi-echelon supply networks and existing ERP investments.
Features
- Cognitive clock engine that compresses 72-hour coordination loops into 3.82-second decision cycles
- Multi-agent orchestration with role-specific AI agents (supply chain, finance) that evaluate trade-offs and reach consensus
- Autonomous anomaly detection and alternative route scanning from live telemetry feeds
- Human-validated decision workflow with explainable rationale for every action
- ERP-agnostic payload formatting and direct broadcast to systems like SAP
- Use-case scout AI that identifies operational pain points and quantifies potential EBITDA, working capital, or cycle-time impact