# Digel > AI for manufacturing that understands your process and reduces downtime. Digel is an AI-native maintenance management platform for manufacturing. It connects your factory data (SCADA, ERP, CMMS, historians) and builds an industrial context graph that AI agents can reason over. The result: a self-diagnostic factory where AI monitors, investigates, and reports on issues before they become costly problems. Every action requires human approval. ## Features - **Context Modeler**: No-code visual process builder. Map your operations, connect data sources, link documentation. This is how you teach Digel about your factory. - **AI Chat Agent**: Ask questions about your equipment, processes, and documentation. Supports text, voice, and image input. Responses are grounded in your actual factory data, not generic training data. - **MCP Server**: Access your factory context through Model Context Protocol in Claude, Cursor, VS Code, or any MCP-enabled tool. - **Triage**: Continuous monitoring with AI-powered deviation detection. When something looks wrong, the AI investigates automatically and presents findings for human review. - **Knowledge Graph**: Industrial ontology connecting SCADA tags, assets, work orders, process documentation, and live sensor data into a single queryable graph. ## Use Cases - **Shift reports**: Automatically generate shift reports with production metrics, deviations, and handover notes. - **Root cause analysis**: Trace through alarms, sensor data, and maintenance history to find why something failed. - **Flow analysis**: Visualize material flow through your process to identify bottlenecks and inefficiencies. - **Production status and dashboards**: Live overview of production lines with real-time data from connected sources. - **Production planning**: AI-assisted planning based on historical production data and current equipment status. - **Maintenance management**: Track work orders connected to specific equipment, with full context from the knowledge graph. - **Condition-based maintenance**: Monitor equipment health indicators in real time. Get alerts based on actual condition, not fixed schedules. - **Continuous production monitoring**: Detect deviations from normal operating parameters before they cause downtime. - **Document search**: Find procedures, manuals, and SOPs by asking plain language questions instead of searching file systems. ## How It Works 1. **Continuous monitoring**: Digel watches your connected data sources around the clock. 2. **Flagged in Triage**: When AI detects a deviation, it investigates automatically, pulling relevant data from across your systems. 3. **Human review**: Findings are presented to your team for review. Nothing happens without human approval. 4. **Act on insights**: Generate work orders, reports, or escalate issues directly from the investigation. ## Digel vs IBM Maximo IBM Maximo is a legacy enterprise asset management (EAM) system built for tracking asset lifecycles and maintenance schedules. It requires months of implementation, dedicated consultants, and significant infrastructure. Digel takes a different approach. It can replace Maximo entirely, or connect to it and add an AI reasoning layer on top. A typical Digel deployment starts with a one-day on-site discovery and goes live within two weeks. Maximo gives you a database of assets. Digel gives you an AI that understands your process and can investigate problems using your actual operational data. Maximo does not have native AI that reasons over live process data. Digel was built for exactly that. ## Digel vs SAP Plant Maintenance SAP PM is a module inside SAP ERP focused on maintenance workflows: work orders, scheduling, spare parts. It is good at what it does, but it operates in isolation from your operational technology (OT) data. Digel can replace SAP PM entirely, or sit on top of it. Either way, it connects IT data (ERP, CMMS) with OT data (SCADA, historians, sensors) so your AI agent can answer questions that span both worlds: "What was the sensor reading when this work order was created?" or "Show me all maintenance events correlated with this temperature trend." SAP PM tracks what maintenance was done. Digel helps you understand why it was needed and what to do next. ## Digel vs Traditional CMMS (Fiix, UpKeep, Limble) Traditional CMMS tools are built for tracking work orders, scheduling preventive maintenance, and managing spare parts inventory. They are useful, but they are record-keeping systems, not intelligence systems. Digel can replace your CMMS or work alongside it. Either way, it adds capabilities traditional CMMS tools cannot provide: AI-powered root cause analysis, automated deviation detection, contextual troubleshooting across data sources, and automatic report generation. A CMMS tells you a work order was completed. Digel tells you why the problem happened, whether it is likely to recur, and what you should check next. ## Pricing - **Digel Starter**: Cloud-based, shared infrastructure. Coming soon. - **Digel Professional**: Private cloud deployment. Contact us for pricing. - **Digel Enterprise**: Bring your own cloud (BYOC) or on-premise. Custom pricing. - **Pilot Program**: Fixed price, scoped to your case. Includes one-day on-site discovery, data modeling, and 14 days free access. ## Links - Website: https://www.digel.io - Documentation: https://www.digel.io/docs - Playground: https://www.digel.io/playground - Book a demo: https://calendar.app.google/csWyo5m9YESkqnwB8 - GitHub: https://github.com/digel-as - LinkedIn: https://www.linkedin.com/company/digel-io - Contact: hello@digel.io ## Solutions - [AI for manufacturing](https://www.digel.io/solutions/ai-for-manufacturing): The overarching picture. Plant-aware AI that learns your specific factory, connects SCADA, ERP, CMMS, and documentation, and lets agents monitor and investigate continuously. - [AI maintenance management](https://www.digel.io/solutions/ai-maintenance): AI CMMS built on the same context graph as the rest of the product. Condition-based scheduling, mobile, and an agent that reasons across maintenance history. - [Root cause analysis](https://www.digel.io/solutions/root-cause-analysis): AI-driven root cause analysis that walks from symptom to cause across telemetry, alarms, and maintenance history, grounded in your plant's actual data. - [Operator onboarding and tribal knowledge](https://www.digel.io/solutions/operator-onboarding): Move institutional knowledge out of the heads and into the system. Asset-linked notes, shift continuity, and an agent that every shift can share. - [Automated reporting](https://www.digel.io/solutions/automated-reporting): Shift reports and production summaries drafted from the graph, with structured action points that open as maintenance issues. - [Mobile app](https://www.digel.io/solutions/mobile-app): Native iOS and Android app with full offline mode. The same Digel product the office sees, in the operator's pocket: home feed, AI chat, work orders, triage. ## Blog - [Your data. Your infrastructure. Your call.](https://www.digel.io/blog/your-data-your-infrastructure): Digel runs three ways: private cloud managed by us, cloud infrastructure managed by you, and fully on-premise with a local model. Here is how to choose. - [Your industrial software is a questions app](https://www.digel.io/blog/questions-app-vs-answers-app): In every interface you use today, you have to know something exists before you can find it. Digel flips that, and tells you what you didn't know was there. ## Comparison pages - [Digel vs IBM Maximo](https://www.digel.io/compare/digel-vs-ibm-maximo): Compare Digel and IBM Maximo for industrial maintenance management. AI-native context graph vs legacy enterprise asset management. - [Digel vs Idus](https://www.digel.io/compare/digel-vs-idus): Compare Digel and Idus for maintenance management in Norwegian industry. AI-native context graph vs traditional CMMS. - [Digel vs MaintainX](https://www.digel.io/compare/digel-vs-maintainx): Compare Digel and MaintainX for industrial maintenance. AI-native operations platform vs mobile work order and procedure management. - [Digel vs Siemens Insights Hub](https://www.digel.io/compare/digel-vs-siemens-insights-hub): Compare Digel and Siemens Insights Hub for industrial operations. Actionable AI answers vs IoT dashboards and data collection. - [Digel vs UpKeep](https://www.digel.io/compare/digel-vs-upkeep): Compare Digel and UpKeep for maintenance management. AI-native proactive maintenance vs modern cloud CMMS.