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Customer service · Unicall / Unimed Paraná

Voice support with continuity for the team.

Authorization status checks and duplicate payment slips, with context passed along.

Organization
Unicall / Unimed Paraná
Operation
Voice and customer service
Agent / delivery
Júlia
Systems and context
Interactive voice response (IVR/URA), lookup systems, and voice infrastructure.
Human involvement
Receiving the context and taking over requests outside the supported workflows.

Júlia was applied to Unicall's voice channel in the Sistema Unimed Paraná. The initial scope is concrete: sending payment slips and checking the authorization status of a healthcare authorization request. The operation combines member identification, system lookups, and transfers with context when a person needs to take over.

The problem

Many calls went directly to customer service representatives. Queues and repeated information increased the effort required from members and the operation.

What was put into practice

Júlia was integrated into the interactive voice response system (IVR, known as URA in Portuguese) to identify the member and handle defined workflows, such as checking the status of an authorization request and duplicate payment slips. Requests are routed according to the need.

  1. 01Voice request
  2. 02Member identification
  3. 03Lookup or sending within the covered workflow
  4. 04Transfer with context when needed
Process summarized from the published account.

From a voice request to two defined workflows

The account describes an operation in which many calls went directly to human customer service. Queues and repeated context increased the effort required from members and representatives. The solution introduced an agent integrated into the interactive voice response system (IVR, known as URA in Portuguese) to recognize and route requests.

Before transactions, the source describes validation of the member's identity. For the covered tasks, Júlia uses the APIs for payment slips and for authorization status lookups. Checking the status of an authorization is the published scope; deciding whether to authorize care is not assigned to the agent.

The status given to the member is what the source system returns through the API. The agent does not generate or infer a status it could not read: when the lookup fails or the request is out of scope, the call is transferred with the identification and the information collected. Deciding whether to authorize care remains out of scope.

The person receives the point where the conversation stopped

When a transfer occurs, the representative receives the identification and the information collected. The case describes routing to the appropriate queue, seeking to reduce transfers between departments and repeated work. The destination queue and the information that reaches the representative are part of the operation's design.

The source cites interactive voice response (IVR/URA) and telephony, identity validation, ElevenLabs speech synthesis, Google Gemini, and Oracle Cloud infrastructure. It also describes recording, call categorization, and monitoring dashboards. Together, these connect the voice conversation to the systems needed for the two workflows.

Monitoring and results within the scope

The case reports reduced handling time in the automated workflows, resolution of some calls, and better routing to queues. Monitoring separates the time spent on each interaction from the share of calls resolved by the agent, allowing both speed and the solution's reach to be examined.

The production rollout took place in phases, interspersed with adjustments and joint learning. Call categorization and dashboards help identify recurring requests and opportunities for improvement. Reimbursement and contract verification appear as next steps, subject to new workflows and integrations.

The reported result and its scope

Average handling time went from 8 minutes to 2 minutes in the automated workflows. In the first month of operation, 12% of calls were fully resolved by the AI. The source also reports less repeated work in call routing.

The indicators belong to this operation and the automated workflows described, not to all Unicall calls or to Starya's other operations. The case does not demonstrate control over actions by agents built outside this solution.

The lesson learned

When a situation needs a person, the data and history are passed along with the handoff. The experience is designed as a shared operation between AI and the team.

Press coverage

Gazeta do Povo covered the delivery of StaryaAI's project for Unimed Paraná, with Júlia on the IVR (URA), in an article published on October 28, 2025.

Read the Gazeta do Povo article (in Portuguese) ↗

What questions does this case help you ask?

  • Which requests have a sufficiently clear beginning and end for an initial scope?
  • How does the representative receive the identification and the unresolved issue?
  • What record distinguishes a completed lookup from an attempt without confirmation?

Published indicators and testimonials

Case study published by Starya. The text, indicators and testimonials below belong to the scope described. They are not independent validation or a guarantee of results in another operation.

Contact center specialized in supplementary health · 850K beneficiaries · 18 cooperatives

Key Results

AHT reduction per call
−75%
Calls fully resolved by AI
12%
Members in the Unicall / Unimed Paraná operation
850KThis figure describes the operation’s member base, not the number of people served by the agent.
Unimed cooperatives integrated
18

The Challenge

Unicall's voice channel received a high volume of calls that went directly to human agents. Without an automation layer, beneficiaries waited in queues and had to repeat the context of their issue. The average handling time (AHT) reached 8 minutes per call, with peaks of up to 30 minutes in more complex situations. This directly impacted the beneficiary experience and raised the operational cost per contact.

Understanding the operation

Júlia resolves in 2 minutes what took 8 with human agents.

The Results

In the first month of operation, 12% of calls were fully handled and resolved by Júlia, with no human intervention. AHT dropped from 8 minutes in the traditional model to 2 minutes in automated service, eliminating queues for covered flows.

What they say about the project

In the first days of operation, Júlia already showed impressive results: 12% of all calls were fully handled by the virtual assistant, with an average resolution time of just 2 minutes, while traditional human service takes an average of 8 minutes. These numbers reinforce the potential of the technology and the success of voice integration, but also reveal new opportunities for continuous learning and optimization. The more Júlia interacts, the smarter and more efficient she becomes.

Rubens Rodrigues Jr. — Representative · Unicall Solutions

We innovatively delivered the voice-based service center to handle day-to-day demands with agility and kept the human element for the moments that matter most: complex cases that require close, welcoming, and personalized contact.

Fernando Morad — COO · StaryaAI

The core of the solution is our proprietary NebulaOS architecture. We built an orchestration platform that allows us to combine the best voice technologies, like ElevenLabs, and AI, with Google Gemini, on a robust Oracle Cloud infrastructure. This translates into a scalable and resilient operation that simplifies complexity and delivers security and agility as fundamental values to our client and their beneficiaries.

Vinícius Reis — CTO and Founder · StaryaAI

Source of the account

Summary of the case published by Starya. Results and scope belong to this operation; they are not a guarantee for other contexts.

Further reading

Continue exploring this context

Learning path

Operate with the team

Connect service, communication and continuity.

Service and teamCommunication
Reference material