Use Cases

Claims & Incidents

Autonomous resolution of customer inquiries and claims

Initial project approach

The challenge

The traditional model presents clear limitations:

  • High volume of manual inquiries
  • Heavy dependency on human agents with fragmented knowledge
  • Navigation across multiple systems (3+ systems per case)
  • Long handling times (average AHT ~6 minutes)
  • Inconsistent response quality
  • Linear increase in operational costs as demand grows

The result is a fragmented and costly operation that is difficult to scale without compromising service quality.

Objective

To transform the customer care operation into a system of cognitive agents capable of autonomously resolving inquiries, reducing response times, eliminating operational friction, and improving overall service efficiency.

Contexto del sector

El sector salud y los sistemas de seguridad social gestionan un elevado volumen de consultas ciudadanas relacionadas con trámites administrativos y asistenciales, donde la rapidez y la precisión son críticas. Sin embargo, gran parte de las interacciones sigue resolviéndose con procesos manuales y sistemas desconectados, lo que incrementa tiempos de atención y limita la eficiencia operativa.

El desafío

The traditional model presents clear limitations:

  • High volume of manual inquiries
  • Heavy dependency on human agents with fragmented knowledge
  • Navigation across multiple systems (3+ systems per case)
  • Long handling times (average AHT ~6 minutes)
  • Inconsistent response quality
  • Linear increase in operational costs as demand grows

The result is a fragmented and costly operation that is difficult to scale without compromising service quality.

Objetivo

To transform the customer care operation into a system of cognitive agents capable of autonomously resolving inquiries, reducing response times, eliminating operational friction, and improving overall service efficiency.

Impact

Automated FRT reduced from 2 minutes to

5 seconds

-25%

overall AHT in handled inquiries

100%

traceable and auditable actions

-35%

operational costs

Así es cómo lo resuelve Alan

Voxi is a cognitive voice agent designed for complex customer service environments.

It operates as an intelligent layer that understands, reasons, and autonomously executes end-to-end resolution of customer inquiries without human intervention.

  • Interpret user intent in natural language
  • Cross-check, query, and verify information across multiple systems
  • Execute end-to-end resolution of complex inquiries
  • Maintain full traceability of every interaction
  • Interpret user intent in natural language
  • Cross-check, query, and verify information across multiple systems
  • Execute end-to-end resolution of complex inquiries
  • Maintain full traceability of every interaction

How Alan solves it

Voxi is a cognitive voice agent designed for complex customer service environments.

It operates as an intelligent layer that understands, reasons, and autonomously executes end-to-end resolution of customer inquiries without human intervention.

Interpret user intent in natural language

Cross-check, query, and verify information across multiple systems

Execute end-to-end resolution of complex inquiries

Maintain full traceability of every interaction

When operations stop reacting and start thinking, the system becomes more agile, more efficient, and more scalable.

CX Insights

Quality analysis and business intelligence reports.

AlanX

Cognitive copilot for human advisors

Real-time assistance for human agents.

HiveAcademy

AI-powered agent training platform

Continuous training and performance improvement for human agents and the system.

Kira

Metahuman AI agent

Metahuman assistant for query resolution.

SideKick

Customer support copilot

Cognitive support for complex case resolution and decision-making.

Charli

Text-based conversational agent

End-to-end support across digital channels.

Voxi

Cognitive voice agent

Voice-based support with autonomous reasoning and full query resolution.