Sellbie is a Brazilian AI-powered CRM platform serving over 130 retail brands — including Mr. Cat, Ortobom, Democrata, Cantão, Via Mia, and Orthopride — structured around a lifecycle model of Optimize, Relate, and Retain to drive customer revenue growth through intelligent automation.
Company
Sellbie
Timeline
2024
—
2025
Role
Product Designer
Project overview
When I joined Sellbie, the company had a strong commercial foundation and a powerful CRM engine, but no structured Product Design practice.
The platform supported complex capabilities such as RFV segmentation, omnichannel campaign automation, inventory intelligence, and AI-generated recommendations. However, user experience was fragmented, data visualization lacked clarity, workflows were overly complex, and there was no design system in place. Additionally, performance metrics were limited to high-level business KPIs, with little structured visibility into product usability and platform efficiency.
I was hired to:
Lead a complete CRM experience redesign
Establish and scale a Design System
Design and operationalize the Sellbie AI Copilot
Build a structured Data Visualization layer
Introduce product and design performance metrics
The goal was to transform Sellbie from a technically capable system into a mature, scalable product.
Challenges
1. Complex CRM Workflows Without Usability Structure
The platform combined segmentation logic, LGPD compliance, cashback strategies, omnichannel communication, and AI-driven automation — but without clear UX structure.
The redesign focused on:
Simplifying campaign creation flows
Reducing cognitive overload
Making segmentation logic more transparent
Structuring omnichannel orchestration
Improving adoption of advanced features
The challenge was not simplification by reduction — it was simplification by structure.
2. Designing the AI Copilot Experience
The “Robô Sellbie” was technically robust but lacked an interaction model.
I designed the AI Copilot as an embedded assistant within campaign workflows, ensuring:
Clear entry points
Explainable AI recommendations
Human oversight and control
Guided automation instead of black-box execution
This transformed AI from a backend feature into a visible productivity driver.
3. Weak Data Visualization & Decision Support
CRM success depends on clarity of performance data.
I led the creation of a structured Data Visualization layer, including:
Revenue attribution dashboards
Campaign performance tracking
Customer retention and reactivation metrics
Omnichannel engagement analytics
Cohort and RFV-based segmentation views
The focus was transforming raw metrics into actionable insights.
4. Absence of Product & Design Metrics
Before my involvement, the company tracked mainly high-level KPIs (revenue, contracts, churn), but lacked structured product performance metrics.
I introduced a metrics framework aligned with CRM best practices, including:
Product & UX Metrics
Feature adoption rate
Campaign creation completion rate
Time-to-launch (campaign setup duration)
Task success rate
Error rate in campaign configuration
AI usage rate
Dashboard engagement rate
Business-Linked Metrics
Campaign conversion rate
Retention rate
Reactivation rate
Average ticket increase
LTV impact from automation flows
This created visibility into how design decisions influenced business outcomes.
5. Establishing a Scalable Design System
Sellbie had no standardized UI system, leading to inconsistencies and slower delivery cycles.
I structured the Sellbie Design System using:
Tailwind CSS as the utility foundation
Shadcn components as scalable primitives
Modular design tokens
Data-heavy UI patterns optimized for CRM interfaces
Reusable layout and interaction patterns
This approach:
Reduced visual inconsistencies
Improved engineering collaboration
Accelerated delivery speed by over 40%
Reduced rework and UI debt
Results
The project repositioned Sellbie as a more mature and scalable CRM platform.
Key outcomes:
Full CRM redesign improving usability and adoption
AI Copilot successfully integrated into campaign workflows
Structured Data Visualization layer increasing analytical usage
Design System implemented with Tailwind + Shadcn foundation
Over 40% acceleration in delivery cycles
Clear product performance metrics enabling data-driven iteration
Stronger alignment between UX decisions and business KPIs
Beyond interface improvements, this project formalized Product Design as a strategic function inside Sellbie — transforming design from execution support into a measurable driver of product evolution.
Sellbie is a Brazilian AI-powered CRM platform serving over 130 retail brands — including Mr. Cat, Ortobom, Democrata, Cantão, Via Mia, and Orthopride — structured around a lifecycle model of Optimize, Relate, and Retain to drive customer revenue growth through intelligent automation.
Company
Sellbie
Timeline
2024
—
2025
Role
Product Designer
Project overview
When I joined Sellbie, the company had a strong commercial foundation and a powerful CRM engine, but no structured Product Design practice.
The platform supported complex capabilities such as RFV segmentation, omnichannel campaign automation, inventory intelligence, and AI-generated recommendations. However, user experience was fragmented, data visualization lacked clarity, workflows were overly complex, and there was no design system in place. Additionally, performance metrics were limited to high-level business KPIs, with little structured visibility into product usability and platform efficiency.
I was hired to:
Lead a complete CRM experience redesign
Establish and scale a Design System
Design and operationalize the Sellbie AI Copilot
Build a structured Data Visualization layer
Introduce product and design performance metrics
The goal was to transform Sellbie from a technically capable system into a mature, scalable product.
Challenges
1. Complex CRM Workflows Without Usability Structure
The platform combined segmentation logic, LGPD compliance, cashback strategies, omnichannel communication, and AI-driven automation — but without clear UX structure.
The redesign focused on:
Simplifying campaign creation flows
Reducing cognitive overload
Making segmentation logic more transparent
Structuring omnichannel orchestration
Improving adoption of advanced features
The challenge was not simplification by reduction — it was simplification by structure.
2. Designing the AI Copilot Experience
The “Robô Sellbie” was technically robust but lacked an interaction model.
I designed the AI Copilot as an embedded assistant within campaign workflows, ensuring:
Clear entry points
Explainable AI recommendations
Human oversight and control
Guided automation instead of black-box execution
This transformed AI from a backend feature into a visible productivity driver.
3. Weak Data Visualization & Decision Support
CRM success depends on clarity of performance data.
I led the creation of a structured Data Visualization layer, including:
Revenue attribution dashboards
Campaign performance tracking
Customer retention and reactivation metrics
Omnichannel engagement analytics
Cohort and RFV-based segmentation views
The focus was transforming raw metrics into actionable insights.
4. Absence of Product & Design Metrics
Before my involvement, the company tracked mainly high-level KPIs (revenue, contracts, churn), but lacked structured product performance metrics.
I introduced a metrics framework aligned with CRM best practices, including:
Product & UX Metrics
Feature adoption rate
Campaign creation completion rate
Time-to-launch (campaign setup duration)
Task success rate
Error rate in campaign configuration
AI usage rate
Dashboard engagement rate
Business-Linked Metrics
Campaign conversion rate
Retention rate
Reactivation rate
Average ticket increase
LTV impact from automation flows
This created visibility into how design decisions influenced business outcomes.
5. Establishing a Scalable Design System
Sellbie had no standardized UI system, leading to inconsistencies and slower delivery cycles.
I structured the Sellbie Design System using:
Tailwind CSS as the utility foundation
Shadcn components as scalable primitives
Modular design tokens
Data-heavy UI patterns optimized for CRM interfaces
Reusable layout and interaction patterns
This approach:
Reduced visual inconsistencies
Improved engineering collaboration
Accelerated delivery speed by over 40%
Reduced rework and UI debt
Results
The project repositioned Sellbie as a more mature and scalable CRM platform.
Key outcomes:
Full CRM redesign improving usability and adoption
AI Copilot successfully integrated into campaign workflows
Structured Data Visualization layer increasing analytical usage
Design System implemented with Tailwind + Shadcn foundation
Over 40% acceleration in delivery cycles
Clear product performance metrics enabling data-driven iteration
Stronger alignment between UX decisions and business KPIs
Beyond interface improvements, this project formalized Product Design as a strategic function inside Sellbie — transforming design from execution support into a measurable driver of product evolution.