How much energy does E-RAN save? Typically 10–15% of RAN energy OpEx — around $450K/year per 1,000 sites at a typical 3 kW site load and $0.15/kWh. Actual savings depend on network topology, traffic profile, and current optimization level; the assessment phase gives a site-specific projection before any commitment.
Do I need new hardware? No. E-RAN is a pure software solution that uses energy-saving capabilities already present in your RAN (e.g. carrier/cell sleep, MIMO adaptation), orchestrated by AI instead of static policies.
Will it degrade network quality? E-RAN operates inside QoS guardrails you define (availability, latency, coverage). Every action is first evaluated in the world model — a calibrated AI-native simulation of your network — and the guardrail supervisor rolls back automatically if measured KPIs deviate from predictions. Reference deployments show zero reliability impact.
Which RAN vendors are supported? Nokia, Ericsson, Huawei, and Open RAN, via vendor-specific ingest and execution adapters. Mixed-vendor networks are supported.
How is it deployed? As containers on Red Hat OpenShift (validated — see our Red Hat Ecosystem Catalog page), preferentially in your private cloud, connected or air-gapped. See the Quick Start guide.
What data does it need, and is subscriber data involved? Equipment-level performance and energy counters from your OSS (Kafka, SFTP, or vendor API). No subscriber communications or identity data is collected or processed.
Does it change my network automatically? Only if you enable it. E-RAN starts in recommendation (shadow) mode; closed-loop execution is opt-in per site group, gated by guardrails with automatic rollback.
How long until we see savings? The assessment quantifies potential in weeks from exported data. A pilot on selected sites validates measured savings; scale-out follows with the option of performance-based pricing.
What does it cost? Assessment-based engagement with the option of performance-based pricing at scale, where you pay from realized savings. Request an assessment at crucible.ag.
What about CO₂? A 12% energy reduction avoids roughly 15,000 t CO₂ per 10,000 sites per year at IEA-average carbon intensity; more in coal-heavy grids.
Is training done on my network? Policies are trained in simulation against your calibrated world model — not by experimenting on the live network. GPU nodes are optional and only needed for on-cluster training; recommendation mode runs on CPU.
How is the deployment secured? Runs under OpenShift's default restricted security context (non-root, no privileged containers), TLS on external interfaces, all state on your cluster. Details in the Deployment & Architecture guide.