SUCCESS STORY
With Cloud Humans, Lia transformed an operation pressured by a more than 50% increase in ticket volume, implemented AI in 15 days, and freed the CX team to focus more on renegotiation, campaigns, and complex cases.
Metric | Result |
|---|---|
Average retention in support tickets | 70% |
CSAT with AI | 88% |
First response time | < 30s |
About Lia
Lia is a checkout for info-product creators and digital businesses, supporting partners in steps ranging from credit and risk analysis to payment management, settlements, collections, and delinquency control.
In this context, the CX team mainly operates on the B2C side, serving the company's partners' students and customers throughout the journey.
This places the team in an operation where quick responses, empathy, and the right guidance make a difference both to the experience and to revenue retention.
When growth starts putting pressure on operations
As the operation grew, Lia began dealing with an increasingly large volume of tickets in a sensitive journey related to payments, collections, and renegotiation. In this scenario, much of the CX team’s effort was still consumed by repetitive demands and by the constant monitoring of the operation.
Title | Description |
|---|---|
High volume of repetitive tickets | Much of the operation was consumed by simple, recurring questions, which took the CX team’s focus away from more strategic support. |
Low retention in the previous model | Lia had a triage bot that retained only about 4% of tickets, with limited impact on the operational workload. |
Operation in firefighting mode | As demand grew, the team had to put out fires, manage queues, and track response times every day, with little room to operate strategically. |
Difficulty scaling without growing the team | Ticket volume increased by more than 50%, but the operation needed to absorb that growth without expanding the team at the same rate. |
More than just gaining speed, the operation needed to redistribute the team’s effort better, shifting focus away from repetitive work and making room for support with more context, sensitivity, and business impact.
“We had a bot that triaged tickets by topic so they would reach human support with a bit of context […] but it retained very little, about 4%.”
James Silva
Head of CX at Lia
From limited triage to a true AI-powered CX operation
To address this scenario, Lia relied on Cloud Humans' support to increase the retention capacity of support tickets, reduce the burden of repetitive requests on the team, and create a more scalable operation.
Main changes:
AI began to absorb a large share of simple and repetitive questions, reducing the human team's dependence for basic support.
The CX team stopped focusing its energy on the simpler operational volume and gained room to work on renegotiation, recovery campaigns, and more complex cases.
The operation began responding to customers much faster, with first responses in under 30 seconds in AI-handled support.
Support began to maintain high satisfaction even with automation, showing that speed and quality could coexist in the new operation.
Support began to maintain high satisfaction even with automation, showing that speed and quality could coexist in the new operation.
During the process, Cloud Humans helped Lia identify where there was real retention potential, structure a knowledge base aligned with the operation, and launch a customized AI for the brand's context. As a result, automation stopped serving only as a triage tool and began resolving much of the support more smoothly, opening space for new automation scenarios throughout the operation.
“Cloud Humans transformed our operation, and very quickly.”
James Silva
Head of CX at Lia
How Lia got AI up and running
The implementation was designed to be feasible within the operation's own routine, without relying on a parallel structure or a heavy technology project. With this, CX leadership was able to lead the process together with Cloud Humans, which helped speed up the introduction of AI into the operation without increasing internal complexity.
Stage | Title | Description |
|---|---|---|
01 | Diagnosis of retention potential | With help from Cloud Humans, they started by mapping which tickets already had real potential to be absorbed by AI, even before deeper integrations. |
02 | Rapid structuring of the knowledge base | With this diagnosis in hand, Lia organized the operation's knowledge base with support from Cloud Humans, in a smooth process with asynchronous tasks. |
03 | Simple go-live without relying on technology | The implementation went live in 15 days, without requiring direct involvement from the technology team and with close support from Cloud Humans throughout the setup. |
04 | Continuous auditing and operational evolution | After going live, AI began to be continuously refined by the CX team, with ticket auditing, quick feedback in the hub, and expansion to flows integrated with API. |
Interesting note:
One interesting detail about this process was the personalization of the assistant with the name “Lia,” the same as the brand. This helped bring the experience closer to the context of the operation and reinforced the perception of smooth service, to the point that many customers didn't even realize they were talking to an AI.
“With your support, I didn't even need help from a dedicated technology person. I was able to handle the implementation myself.”
James Silva
Head of CX at Lia
With the base validated in the operation, Lia began to expand the use of AI to flows more connected to the business routine. Among the examples are scenarios such as cancellation with automatic partner identification and second-copy issuance via API, showing how the operation went beyond triage and the FAQ.
The impacts of Lia (ClaudIA) on operations
The introduction of AI increased Lia’s retention capacity in support tickets and brought an immediate gain in service speed. At the same time, the operation maintained a high level of satisfaction and gave the human team more room to work on renegotiation, campaigns, and more complex requests. The effects showed up both in operational metrics and in the way CX began to contribute to the experience and to revenue retention.
Metric | Result | Impact |
|---|---|---|
Average retention on tickets | 70% | Consistent absorption of support handled by AI |
CSAT with AI | 88% | Quality maintained with automation |
First response with AI | <30s | Nearly immediate response in 100% of cases |
These results become even clearer when retention is analyzed alongside the shift in the team’s role. With AI absorbing much of the repetitive support, the team began to dedicate more energy to renegotiation and actions related to revenue recovery. This helps explain why the impact of Cloud Humans appears not only in service efficiency, but also in the way the operation better distributes effort, speed, and context between AI and humans.
“This result allowed us to identify new automation scenarios and boost the performance of the human team, especially in revenue recovery actions via WhatsApp campaigns.”
James Silva
Head of CX at Lia
What can an AI-powered CX operation teach
Topic | Description |
|---|---|
Well-applied AI does not reduce personalization | Lia’s experience showed that AI does not have to make service colder or more generic. When properly configured, it preserves fluidity, context, and brand alignment. |
Removing the repetitive frees up human value | By taking over much of the recurring questions, AI makes room for the team to work more deeply on renegotiation, campaigns, and complex cases. |
The operation improves with continuous refinement | The best results came when AI began to be monitored, audited, and adjusted frequently, instead of being treated as static automation. |
The best path is to start small and evolve | The operation’s own recommendation is to start with simpler scenarios, validate how AI works, and gradually expand into more complex flows. |
Cloud Humans responds
What was the operation like before Cloud Humans?
There was a triage bot with about 4% retention, a lot of repetitive volume, and an overloaded team, with difficulty scaling without growing proportionally.
Was the implementation process complex?
No. The implementation took 15 days, was simple, had close support from Cloud Humans, and did not require direct involvement from the technology team.
Did AI hurt service personalization?
No. The main lesson was exactly the opposite: AI enhanced personalization, with a fluid and empathetic experience.
What results did Lia see with AI?
42% retention in the first month, an average of 70% retention in support tickets, 88% CSAT with AI, and first response in less than 30 seconds in 100% of cases.
In what type of service did AI start to help?
In repetitive questions, cancellation flows with identification via API, and sending a duplicate boleto via API, among other scenarios.
