SUCCESS STORY
With Cloud Humans, Lia transformed an operation strained by a more than 50% increase in ticket volume, implemented AI in 15 days, and freed the CX team to focus more on renegotiations, 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
With the growth of the operation, Lia began dealing with an ever-growing volume of tickets in a sensitive journey related to payments, billing, and renegotiation. In this scenario, much of the CX team's effort was still consumed by repetitive requests and 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 operational workload. |
Firefighting mode operation | As demand grew, the team had to put out fires, manage queues, and monitor response times daily, 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 pace. |
More than just gaining speed, the operation needed to redistribute the team's effort better, shifting focus away from repetitive tasks 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 retention was very small, about 4%.”
James Silva
Head of CX at Lia
From limited triage to a true AI-powered CX operation
To face this scenario, Lia relied on Cloud Humans' support to increase the retention capacity of support service interactions, reduce the burden of repetitive requests on the team, and create a more scalable operation.
Main changes:
AI began to absorb much of the simple and repetitive questions, reducing the team's dependence on human agents for basic support.
The CX team stopped focusing energy on the simplest 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 response in less than 30 seconds in interactions handled by AI.
Customer service continued to maintain high satisfaction even with automation, showing that speed and quality could coexist in the new operation.
Customer service continued 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, build a knowledge base aligned with the operation, and launch a custom AI tailored to the brand's context. With this, automation no longer served only for triage and started resolving much of the support more fluidly, 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 viable within the operation's own routine, without relying on a parallel structure or a heavy technology project. With that, CX leadership was able to lead the process together with Cloud Humans, which helped accelerate the introduction of AI into the operation without increasing internal complexity.
Step | Title | Description |
|---|---|---|
01 | Assessment 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 assessment 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 into production, AI began to be continuously refined by the CX team, with ticket audits, quick feedback in the hub, and expansion to flows integrated with API. |
Fun fact:
An interesting detail of 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 operation's context 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 foundation validated in the operation, Lia began expanding the use of AI to workflows more connected to the business's routine. Examples include scenarios such as cancellation with automatic partner identification and issuance of a duplicate via API, showing how the operation began to go beyond triage and 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 appeared both in operational metrics and in the way CX began to contribute to the experience and to revenue retention.
Metric | Result | Impact |
|---|---|---|
Average retention in tickets | 70% | Consistent absorption of support handled by AI |
CSAT with AI | 88% | Quality maintained with automation |
First response with AI | <30s | Near-immediate response in 100% of cases |
The reading of these results becomes even clearer when retention is analyzed alongside the change 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 Cloud Humans’ impact appears not only in service efficiency, but also in the way the operation better distributes effort, speed, and context between AI and human.
“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 doesn't have to make service colder or more generic. When properly configured, it preserves flow, context, and brand alignment. |
Removing repetitive work frees up human value | By handling much of the recurring questions, AI opens space for the team to act more deeply in renegotiation, campaigns, and complex cases. |
Operations improve with continuous refinement | The best results came when AI started 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 to more complex flows. |
Cloud Humans answers
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 harm service personalization?
No. The main takeaway was exactly the opposite: AI enhanced personalization, with a smooth 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 kind of support did AI start to help?
In repetitive questions, cancellation flows with identification via API, and issuing second copies of payment slips via API, among other scenarios.
