Cerebrix provides an AI‑driven Context Engine that connects data from ERP, WMS, demand‑planning and regulatory sources to reason about supply‑chain risks before they materialize. By modeling supplier relationships, part criticality and compliance obligations, the platform can generate actionable insights and prototype new workflows in as little as ten days, delivering up to ten‑fold improvements in speed, cost and accuracy over traditional pilots.
Funding
Funding not disclosed
Founders
Product
Problem
Supply chain management systems capture isolated data streams—such as ERP transactions, warehouse movements, and demand forecasts—but cannot correlate these signals to reveal emerging risks like component shortages, regulatory recalls, or geopolitical disruptions before they materialize.
Solution
Cerebrix offers an AI‑powered Context Engine that ingests data from ERP, WMS, demand‑planning, and regulatory sources and builds a unified, relationship‑aware model of the entire supply network. The engine traverses a pre‑built supply‑chain ontology to reason across supplier dependencies, inventory levels, compliance histories, and external risk indicators, producing actionable disruption forecasts within ten days. Deterministic graph‑based execution ensures consistent outputs, while sovereign deployment keeps all enterprise data behind the firewall. By delivering insights ten times faster and cheaper than traditional analytics, the platform enables enterprises to prototype mitigation strategies rapidly and improve supply‑chain resilience.
Target Audience
Primary customers are large enterprises in life sciences, healthcare, manufacturing, high‑tech, and automotive sectors that manage complex, multi‑tier supply networks and require early warning of regulatory or operational disruptions.
Features
- Unified ingestion layer that connects ERP, WMS, demand‑planning, and regulatory data into a single contextual graph
- Built‑in supply‑chain ontologies (Pharma, MedTech, Industrial, Retail) that provide immediate schema awareness and relationship mapping
- Deterministic graph traversal engine delivering repeatable, non‑probabilistic predictions
- Sovereign deployment option allowing full VPC or private‑cloud installation with zero data leakage
- Real‑time disruption alerts that surface risks such as tier‑3 shortages, recall hazards, and geopolitical events
- Rapid prototyping workflow that generates a working AI model in approximately ten days