Digel vs Idus
Compare Digel and Idus for maintenance management in Norwegian industry. AI-native context graph vs traditional CMMS.
If you work in Norwegian industry, you have probably used Idus or seen it. Many plants across Norway and the Nordics run their maintenance operations on it, and it has done that job reliably for years.
Digel is not a form-based maintenance tracker. It is an AI-native maintenance and quality management platform that connects your factory data into a context graph and uses AI agents to bring you answers before you know to look.
At a glance
| Feature | Digel | Idus |
|---|---|---|
| AI-powered root cause analysis | ||
| Industrial context graph | ||
| Proactive issue detection | ||
| CMMS / work orders | ||
| Preventive maintenance scheduling | ||
| Spare parts management | ||
| Mobile support (PWA) | ||
| Natural language queries | ||
| Connects to SCADA / sensors | ||
| Tribal knowledge capture | ||
| Automated reporting | ||
| QR code asset identification |
How they handle data differently
Idus is form-based. You fill out fields to create work orders, log completed tasks, and manage spare parts. The data sits in tables. If you want to know what happened with a machine, you query those tables.
Digel builds a context graph that connects assets, sensors, documents, maintenance records, and operator notes. When you ask "why did the fryer trip?" the AI walks that graph from the symptom outward, checking sensor trends, maintenance history, and related equipment. The answer comes back with the reasoning attached.
Waiting for you vs watching for you
With Idus, maintenance is reactive or scheduled. Something breaks and you log it. Or you set up a preventive schedule and follow it. The system waits for you to act.
Digel monitors continuously. Agents watch your connected data sources around the clock. When a vibration trend drifts or a temperature pattern looks familiar, the system investigates and presents findings in Triage. The agent proposes, you decide.
Where tribal knowledge goes
In Idus, an experienced operator's knowledge stays in their head. When they leave, it goes with them.
Digel captures that knowledge through notes linked to the context graph and through every conversation operators have with the AI. The more your team uses it, the more the system understands about your specific plant. That knowledge stays in the system and is available to the whole team.
When Idus makes sense
If you need a straightforward CMMS for tracking work orders, preventive schedules, and spare parts inventory, Idus does the job. It is proven, Norwegian, and familiar to most maintenance teams in the Nordics.
When Digel makes sense
If you want AI that understands your process, catches problems before they get expensive, and turns operator experience into institutional knowledge, Digel does that. It can replace Idus entirely or connect to your existing systems and add AI reasoning on top.
Ready to see the difference?
Book a 30-minute demo and see how Digel compares to Idus on your data.