Paraná, Entre Ríos · Argentina
Skip to content
Possition IAPossition IAEfficient AI for Business

Customer service · Telecommunications and services companies

AI chatbots for customer service in call centers

An agent that understands what the customer actually wants, resolves the repetitive part, and hands off to a person with the history already loaded.

Agente de IA Sofía: atiende consultas, lee facturas y se conecta a sistemas
Client
Telecommunications and services companies
Sector
Customer service
Product
Sofía
Services
Chatbot for companiesCustomer service automation

In numbers

35% menos tiempode operador en consultas repetitivas, liberando horas para excepciones y gestiones de mayor valor
1,54xde capacidad de atención con la misma estructura de operadores
18%de costo operativo equivalente del subproceso (no implica automáticamente ahorro de caja)

The initial problem

High interaction volume, repetitive inquiries, wait times, customer frustration, and rising operating costs.

The previous process

Service depended on agents for the full volume, including repetitive inquiries, with rigid IVR flows that did not understand each customer's real intent.

The solution implemented

Conversational agents and voice AI with Dialogflow and CCAI: intent understanding, session handling, mission playbooks, handoff to humans, and flow redesign so the bot is not just an IVR with a voice. The design included humanization tests, dialect variants, approval criteria, a war room, and metrics defined from the start, validated with thousands of user interactions.

How we did it

  1. Map of real intents

    We analyzed the support volume to separate what can be resolved on its own from what needs a person, and defined playbooks by mission instead of an options tree.

  2. Conversational and voice design

    We worked on humanization, session handling, and dialect variants so the agent understands how the customer speaks, not how the script was written.

  3. Handoff with context

    When a query goes beyond the agent, the conversation passes to an operator with the history and data already loaded, without asking the customer to repeat everything.

  4. Testing, metrics, and war room

    We defined approval criteria and metrics before going live, and validated behavior with thousands of real user interactions.

Integrated systems

  • Dialogflow / CCAI
  • Telephony and IVR
  • Digital channels

The result

Automation of repetitive support with intent understanding, intelligent handoff, and 24/7 coverage for telecommunications and services.

What changed in the operation

  • Repetitive queries are resolved without occupying an operator.
  • The customer doesn't repeat the problem when a person takes over.
  • Support stays available outside call center hours.
  • Agent behavior is measured with criteria defined from the start.

Frequently asked questions about this case

How is this different from an IVR with a voice?

An IVR forces the customer to pick options. Here the agent interprets the intent behind what the person says and acts accordingly, even if they don't use the script's words.

What happens when the agent can't resolve it?

It hands off to a human operator with the full conversation history and validated data, so the person picks up where it left off.

How do you measure whether it works?

With metrics agreed before implementation: resolution without human intervention, correct handoffs, and answer quality over real interactions.

Tell us which process you'd like to improve

We'll look at how it works today and send back a concrete diagnostic: what to automate, how, and what result to expect. No cost, no commitment.