Deepgraph AI provides an enterprise context orchestration platform that continuously ingests data from systems like ERP, SIEM, and fraud detection into a unified, graph‑based knowledge layer. By delivering real‑time relational context to AI models via low‑latency APIs, it improves prediction accuracy while keeping raw data on‑premise for security and compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI models often operate on isolated data snapshots, lacking the real-time context needed to make accurate decisions. Disconnected systems such as ERP, SIEM, and fraud detection tools provide fragmented signals, leading to AI that “sees everything, understands nothing.” This context gap results in costly mis‑predictions and security exposure for banks, hospitals, and other large organizations.
Solution
DeepGraph AI offers an enterprise context orchestration platform that unifies data from disparate sources into a live, graph‑based knowledge layer. By continuously ingesting events, transactions, and alerts, the platform supplies AI models with up‑to‑date relational context, enabling them to reason across the full data thread rather than isolated fragments. The solution runs at the edge of the enterprise network, extending the security perimeter to keep sensitive data under the organization’s control while still providing cloud‑scale analytics. DeepGraph’s APIs let existing AI workloads consume the enriched context with minimal code changes, improving prediction accuracy and reducing unnecessary token usage.
Target Audience
Primary customers are large enterprises in regulated sectors—such as banking, healthcare, and insurance—that deploy AI for fraud detection, security monitoring, and operational analytics.
Features
- Real‑time data ingestion from ERP, SIEM, fraud, and other enterprise systems into a unified graph database
- Continuous context enrichment that links events, transactions, and alerts across silos
- Edge‑deployed security layer that keeps raw data on‑premise while exposing only contextual insights to the cloud
- Low‑latency APIs and SDKs for seamless integration with existing AI/ML models and workflows
- Automated lineage and audit trails to support compliance and explainability requirements
- Scalable architecture that handles high‑volume enterprise data streams without performance degradation