SUCCESS STORY
The company faced a major challenge: a growing volume of support requests, many of them repetitive, combined with a support experience that no longer reflected the simplicity of the product. You can see the solution in this case study.
Metric | Result |
|---|---|
Tickets resolved per month | +3 thousand with AI |
Average resolution time | 5 hours (before: 22 hours) |
Ticket retention | +70% resolved by AI |
About Conta Simples
Conta Simples is a fintech created to simplify companies' financial management, promoting greater control over expenses, organization, and financial management.
In customer service, many interactions involve urgent situations, such as difficulties making a payment, transfers, or other financial operations.
In this sense, the CX team needed to handle a large volume of requests, many of which were repetitive. Questions alone accounted for about 35% of the interactions received by the operation.
The customer service experience did not reflect the product
Before the operation transformation, Conta Simples used a traditional chatbot model based on menus and support journeys. In this format, the customer had to navigate through several options before being able to explain their issue.
Challenge | Description |
|---|---|
Rigid support experience | Support was based on a traditional chatbot with menus and multiple options. To explain an issue, the customer had to go through different steps until they could describe what they needed. |
High volume of repetitive questions | A large part of the interactions received by support were related to operational questions or guidance on how to use the platform (about 35%). |
Operation scale limited by the traditional model | As the company grew, the volume of tickets kept increasing. Even with adjustments to the chatbot and attempts to reorganize the operation, the model still had limitations when it came to scaling with quality. |
Support required excessive effort from the team | Even simple questions kept reaching the human team, consuming the team's energy and limiting the operation's ability to act more strategically. |
To handle the increase in volume, Conta Simples even tested alternatives such as reorganizing the support flow and even using outsourced support to absorb part of the demand.
Still, the problem persisted. The model continued to require a great deal of human effort to resolve simple questions, while the company's product promised exactly simplicity and efficiency for its customers.
“The customer had to navigate among options like question, request, or issue until they could explain what they wanted.
This made support slower and less intuitive.”
Amanda Rodrigues
Support Coordinator at Conta Simples
A new approach to customer service
Faced with an operation strained by recurring questions, queues, and a service model that no longer matched the product’s promise of simplicity, Conta Simples decided to reorganize support with AI as part of the operation.
Main changes:
The operation began resolving a significant share of recurring questions with ClaudIA acting on the front line of support.
The human team stopped concentrating energy on repetitive requests and began focusing more on context, quality, and customer experience.
The AI began operating integrated into the operation, with the ability to consult internal systems and support case resolution beyond basic replies.
The operation adopted a continuous audit and improvement routine, monitoring retention, handoff, and opportunities to adjust the AI’s performance.
Support gained more scale without increasing the team’s workload, allowing the company to grow with greater operational predictability.
“AI does not replace people; it frees the team to deliver more strategic support.”
Amanda Rodrigues
Support Coordinator at Conta Simples
From the knowledge base to ClaudIA's continuous evolution
The implementation of AI at Conta Simples started with organizing the knowledge that already existed within the operation.
Since the team already had FAQs and well-defined processes, the focus became teaching ClaudIA how to act based on this content, translating each journey into a clear logic of problem, solution, and tone of voice.
Stage | Title | Description |
|---|---|---|
01 | Structured knowledge base | Before activating ClaudIA, Conta Simples already had FAQs and well-defined processes. The implementation began by turning this knowledge into operational logic for the AI. |
02 | Tone of voice and escalation criteria | The team defined how the AI should respond, what it should handle itself, and which situations should be directed to human support. |
03 | Integration with internal systems | After the first few months handling simpler questions, ClaudIA began connecting to Conta Simples' systems to look up information, recognize customers, and resolve cases with more context and security. |
04 | Auditing and continuous improvement | The operation began to have a dedicated person monitoring what the AI handles, what escalates, and why. Every mistake became a learning opportunity, and every adjustment helped improve the experience and retention. |
As the operation matured, ClaudIA stopped acting only as a resolver of basic questions and began to respond with more context, consult systems, and even anticipate messages in instability scenarios. This turned the implementation into a continuous process of evolution, and not a one-off project.
“We had FAQs and well-defined processes within support. We just needed to teach ClaudIA how to act based on that content.”
Amanda Rodrigues
Support Coordinator at Conta Simples
Transformation into numbers
With ClaudIA integrated into the operation, Conta Simples began to absorb a large share of the simpler, repetitive interactions, reducing the team’s operational effort and speeding up case resolution. The impact was felt both in the efficiency of the operation and in the customer experience.
Metric | Result | Impact |
|---|---|---|
Interactions retained by AI | 70% | Most of the volume began to be resolved without direct human intervention. |
Tickets resolved per month | ~3,000 | Significant reduction in the team’s operational workload. |
Average resolution time | 22h → 5h | AI began absorbing a large share of the interactions without escalation (+23 thousand cases). |
Instead of focusing energy on recurring questions, the team began focusing on critical cases, quality, and improving the customer experience. In practice, this meant a lighter operation, with less overload for the team and more room for strategic work.
AI doesn’t replace people; it frees up the team so they can deliver more strategic support.”
Amanda Rodrigues
Support Coordinator at Conta Simples
What Conta Simples learned in the process
Topic | Description |
|---|---|
AI does not replace people. | By removing the burden of repetitive questions from people, the operation gains room to focus on quality, context, analysis, and customer experience. |
Continuous auditing is central to AI success | Conta Simples understood that monitoring escalations, reviewing responses, and quickly correcting anything unexpected is what ensures quality and long-term operational improvement. |
Scaling support with quality requires balance between AI and humans | The goal was never to keep everything at any cost, but to resolve efficiently and ensure that sensitive or urgent cases were correctly routed to human support. |
There is no good experience with an overloaded team | One of the main gains noticed was the team's operational lightness, which began to work with more focus and less strain. |
“Don’t try to do everything alone. Have AI as your ally. Get out of that operational chair, let it do this, and take a strategic position where you can actually add value to your company.”
Amanda Rodrigues
Support Coordinator at Conta Simples
Cloud Humans answers
Did AI replace Conta Simples’ support team?
No. The company kept the same number of people and started using AI to absorb recurring questions, freeing the team to work with more focus on quality, context, and strategy.
How was ClaudIA trained to serve Conta Simples’ customers?
Implementation started from the knowledge base the company had already structured, such as FAQs and internal processes. From there, the team taught AI the logic of the journeys, the retention and escalation criteria, and the brand voice.
Does AI solve everything on its own?
No. Some cases are intentionally routed to human support, especially when they require deeper investigation, faster action, or greater sensitivity in resolution.
What ensured the operation’s quality after implementation?
Conta Simples set up a routine of continuous auditing of conversations, tracking what AI retained, what escalated, and why. This monitoring became central to correcting errors quickly and evolving ClaudIA’s performance over time.
Does AI work well in a financial operation?
Yes, especially when integrated with the company’s systems. With integrations and queries to internal data, ClaudIA began to operate with more context and security, which is very important in a fintech operation.
