Digel offers an AI‑driven platform that unifies data from SCADA, MES, ERP, CMMS and operator knowledge into a contextual graph, enabling continuous anomaly detection and automated issue triage. Users can query the system in natural language to generate dashboards, root‑cause analyses, shift reports, and maintenance work orders, helping manufacturers improve uptime and reduce maintenance costs.
Funding
Funding not disclosed
Founders
Product
Problem
Manufacturers often rely on fragmented data sources—SCADA, MES, ERP, CMMS, and undocumented operator knowledge—making it difficult to detect early equipment issues, perform root cause analysis, and generate consistent operational reports. This fragmentation leads to delayed maintenance, unplanned downtime, and inefficient decision‑making on the shop floor.
Solution
Digel provides an AI‑driven platform that creates a unified industrial context graph linking sensor streams, equipment hierarchies, work orders, procedures, and operator notes. The AI continuously monitors live data, flags anomalies, and automatically generates plain‑language investigations, shift reports, and maintenance recommendations. Users can query the system in natural language to build dashboards, retrieve documents, or initiate work orders, while the platform integrates with existing OT stacks (SCADA, MES, ERP, CMMS) without replacing them. The result is proactive issue triage, data‑backed root cause analysis, and automated reporting that keep factories running efficiently.
Target Audience
Primary customers are plant managers, maintenance supervisors, and operators in mid‑size to large manufacturing facilities seeking to improve uptime, reduce maintenance costs, and streamline reporting using existing OT systems.
Features
- Continuous anomaly detection across all connected process signals, mirroring senior operator intuition
- Automated issue triage with pre‑populated investigations, sensor history, and one‑click actions
- Conversational dashboards that generate charts and KPI views from plain‑language queries
- Integrated root cause analysis that traverses the context graph to combine telemetry, maintenance history, ERP data, and documentation
- AI‑enhanced maintenance management that creates work orders from chat input and attaches relevant asset data
- Real‑time document search linking SOPs, safety bulletins, and technical manuals to operational queries
- Flexible deployment options (managed cloud, private cloud, on‑premise) to match customer infrastructure and security requirements