Optimizing Grid Distribution and Automating Support for Renewable Utilities – EXAMPLE
Cliente:
Industria:
Timeline:
Aplicado tecnologías:
- Laia (AI Call Center)
- Custom IoT Middleware
- Pitón
- Predictive Analytics
Contexto del cliente
EcoGrid Dynamics is a rapidly scaling green energy provider managing distributed solar and wind networks across the Iberian Peninsula. Serving over 150,000 residential and commercial endpoints, their core mandate is to deliver sustainable, uninterrupted load balancing to their clients while maintaining lean operational overhead.
The Operational Bottleneck
EcoGrid’s rapid geographic expansion resulted in two critical bottlenecks. First, their legacy grid telemetry systems were completely isolated from their modern CRM, trapping vital operational data in silos and making proactive customer communication impossible. Second, during localized weather anomalies, their inbound call centers were heavily overwhelmed by routine status inquiries. This high call volume led to massive abandonment rates and forced highly trained support staff to act as data-entry clerks rather than solving complex infrastructure issues.
La arquitectura de la solución
Deveco engineered a dual-pronged digital transformation strategy to bridge the gap between their operations and customer experience. At the infrastructure level, we designed and deployed secure custom IoT middleware (built in Python) to extract real-time data from their legacy grid monitors and feed it into a unified predictive analytics dashboard.
On the customer-facing side, we integrated Laia—our proprietary AI voice platform—directly into their telecommunications stack. We trained Laia’s natural language models on EcoGrid’s specific technical documentation, empowering the agent to instantly answer live voice queries regarding real-time outage statuses, billing inquiries, and energy usage 24/7, with zero human intervention.
Impacto empresarial
The architectural overhaul transformed EcoGrid from a reactive utility into a proactive technology company. The custom middleware established a unified data pipeline, allowing executives to forecast load imbalances hours in advance. Furthermore, Laia successfully resolved over 12,000 routine tier-1 calls in the first month alone. This automation reduced tier-1 support costs by 35% and freed their human workforce to focus entirely on high-value, strategic grid expansion.