SUCCESS STORY
Understand how Linus prepared customer support for the months of highest demand, scaled with quality, and transformed a reactive operation into a more stable structure with the help of AI.
Metric | Result |
|---|---|
Average retention | 60% |
CSAT with AI | 92% |
Target | 2026 achieved ahead of schedule |
About Linus
Linus is a Brazilian sustainable lifestyle brand, with vegan and recyclable products, and treats CX as a central area for the brand's reputation and customer loyalty.
Internally, customer service is seen as a critical part of the experience, with a direct impact on the results of marketing, logistics, and the other departments.
During promotional periods, the volume of contacts could triple, putting pressure on a small structure at a sensitive point in the customer experience.
When peak traffic became a bottleneck for the experience
With a lean structure and a predictable demand peak on the calendar, Linus needed to deal with support that became more strained precisely when the customer experience became more sensitive.
As demand increased, the operation accumulated bottlenecks that affected the team's pace, the flow between channels, and responsiveness to the customer.
Title | Description |
|---|---|
Huge support queues | The high volume of contacts increased wait times and put the operation in a state of constant urgency. |
Duplicate cases across channels | When support took too long, the customer moved to other channels in search of an answer. This caused the operation to handle the same case more than once. |
Complaints about slow support | The slowness stopped being an internal problem and began to affect the brand's reputation, with complaints on Reclame Aqui. |
Team stuck in reactive mode | The team was consumed by repetitive and urgent demands, with no time to build processes that would prevent new queues. |
In this scenario, Linus's challenge was to create a more stable operation, with less friction in support and greater responsiveness during the most pressured moments. The company needed to find a way to absorb the increase in demand without letting queues, rework, and bottlenecks between channels compromise the customer experience.
At the same time, this change needed to give the team back more control. The goal was not only to gain speed, but to structure an operation capable of responding more consistently, reducing the team's overload, and making room for a less reactive day-to-day approach.
“We didn't have time to be strategic or to build processes that would prevent new queues.”
Luiza Kravtchenko
CX Coordinator at Linus
AI as a solution to reduce friction and scale
To handle peaks with greater stability, Linus implemented Cloud Humans' AI in its CX operation and gave it a name: Lina. The goal was to reduce the burden of repetitive requests, decrease rework across channels, and prepare the team to respond better during periods of higher pressure.
Main changes:
A significant share of recurring questions began to be handled by the AI.
The team gained more room to focus on critical cases.
The operation became less dependent on manual effort to handle basic volume.
Customer service now has a more stable foundation for peak periods.
The team gained more ability to monitor, adjust, and evolve the experience over time.
The solution created a new foundation for customer service, with greater scaling potential and less dependence on manual effort for recurring requests. To capture this gain, implementation needed to be fast, close to the operation, and guided by constant adjustments.
“The AI came in to make the team more strategic, the operation healthier, and customer service more scalable.”
Luiza Kravtchenko
CX Coordinator at Linus
How the new operation took shape in practice
The implementation of Lina happened with a certain urgency and within a strategic window. Instead of waiting for the main end-of-year peak, Linus used September as a laboratory to put the AI into operation, measure its efficiency, and arrive in November with a more prepared foundation.
This process required a quick migration, channel enablement, and a close follow-up routine. More than just putting the AI live, the priority was to help it evolve quickly within the operation.
Stage | Title | Description |
|---|---|---|
01 | The laboratory | Linus decided to bring the implementation forward to test Lina during a high-volume period (September) and use that period to understand what still needed to be adjusted before the year's most critical peak. |
02 | Migration and accelerated pace | The change happened in a short time. The operation moved off the previous platform and, the next day, already needed to enable Cloud Chat and the necessary channels to get the new structure live. |
03 | AI auditing and monitoring | From the beginning, the audit routine became a priority. Linus began reviewing protocols, monitoring responses, identifying failure points, and understanding what needed to be corrected so that Lina could evolve more quickly. |
04 | Lina's evolution in day-to-day operations | With Geovanna, CX analyst, leading the audits and constant adjustments, the AI began to better incorporate the brand's tone, the context of conversations, and the nuances of support. |
The implementation was not treated as an isolated technology project. It was run as part of the day-to-day operation, with close follow-up, constant adjustments, and a clear sense of priority. This model helped Lina evolve faster and allowed Linus to turn an urgent rollout into a more solid foundation for the months of greater pressure.
There is also an important detail here: the AI's evolution depended less on a robust technical structure and more on operational clarity, auditing, and customer-service know-how. It was this combination that helped Lina increasingly speak the language of Linus.
“Sometimes we, as a client, don't know how to say exactly what we want, but I told our story and Cloud Humans, with its AI expertise, translated it into a process.”
Luiza Kravtchenko
CX Coordinator at Linus
What changed with Lina in operation
With Lina in operation and an audit-like routine in place, Linus began to capture concrete gains both in the metrics and in the team's dynamics. The progress showed up in retention, customer satisfaction, and the operation's ability to get through periods of greater pressure with more stability.
Metric | Result | Impact |
|---|---|---|
Average retention in tickets | 60% | More resolution without expanding the team at the same rate. |
Average CSAT | 92% | Scaling while maintaining perceived quality. |
The numbers help to put the result into perspective, but the effect of the implementation also became visible in the day-to-day operation. Lina started with about 39% retention in September, rose to 41% in October and reached 60% in November, showing that the combination of technology, auditing, and fine-tuning generated progressive gains over the months.
At the same time, Linus began operating with less friction during periods of high demand, with fewer duplicate protocols, an end to the red flags on Reclame Aqui for delays, and a more positive perception from the team about the area's routine. Instead of just absorbing volume, the operation gained more predictability and more room to act where human intervention created more value.
“In six years, it was the first time I felt a lighter atmosphere in the area.”
Geovanna Silva
CX Analyst at Linus
What Linus learned by putting AI into operations
Topic | Description |
|---|---|
AI learns from the people who live customer support | The quality gain depends on the expertise of those who know the recurring questions, understand customer context, and can adjust content, intent, and language based on real conversations. |
Continuous auditing is part of the result | Lina's evolution came through frequent review of conversations, term adjustments, creation of new content, and response calibration. This is what made it possible to turn a quick implementation into a more accurate operation over time. |
AI changes the team's way of working | In practice, AI took the burden off repetitive tasks and opened space for more analytical and strategic activities. Instead of focusing the routine on volume, the team began to dedicate more energy to improving operations, reading patterns, and enhancing the experience. |
The impact shows up in the numbers, but it starts in the routine | The results in retention and CSAT are relevant, but the effect goes further: less overload, more predictability, and a lighter operation for those working day to day. It was this change in routine that sustained the improvement in the metrics. |
Cloud Humans answers
Did Linus already use automation before Lina?
Yes. The company already had experience with bots, but was still looking for a smoother operation, with more natural responses and greater ability to handle peak periods without increasing friction in support.
What motivated the change in operations?
The main trigger was the combination of a lean structure and predictable demand spikes, such as when volume increased, the operation accumulated queues, duplicate protocols appeared, and the team lost room to act with more control over the routine.
Did AI replace people on the team?
No. AI's role was to absorb a significant share of repetitive demands and create space for the team to work on the most critical cases, improve the operation, and evolve the experience itself.
What does this case show for other CX operations?
That AI in support works best when introduced as part of an operational change. The result depends on timing, close monitoring, and the team's ability to teach the AI based on the real routine.
