Operational context
Energy, utility and telecom operators combine customer contact with billing, payment follow-up and field operations. At OpenSource Energie, we are building a fully agentic front office for customer contact, support and follow-up. AI agents work with existing systems to retrieve context, prepare replies and carry out agreed steps, with the team in control. On the asset side, we build predictive maintenance models on sensor and maintenance data, so field teams get ahead of failures instead of chasing them.
Where this usually breaks down
- Knowledge is scattered across inboxes, folders, and people
- Document-heavy work still burns too much manual time
- Handovers and approvals slow down execution and follow-up
- Existing systems are present, but they do not work together intelligently
Where this stands today
published case studies in this industry
featured examples on this page
concrete process needed to start well
need for loose AI hype or experiments
Featured case studies
Projects that show how this lands in practice.