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LIVE PROJECT

Gamified microlearning that turns store salespeople into certified brand specialists

LXP built with Next.js 15 and React 19 for Marken Fassi — 10 to 60 second microlearning consumable between customers, gamification with streaks and tiers from Bronze to Black Ruby, FASSI.AI sales assistant with a structured answer base from the catalog, private community for sharing displays among retailers, team performance dashboards and a certification program that positions salespeople as official brand specialists. All in a mobile-first interface designed for the shop floor, with fast loading even on unstable 3G/4G connections.

marken.expostacker.com.br/
CASA FASSI — preview
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Tech stack: Next.js 15 React 19 Tailwind CSS Gamificação FASSI.AI LXP
28%
Sales Increase (est.)
67%
Daily Engagement (target)
78%
Certification (pilot)

Test access

e-mail teste@casafassi.com.br
senha fassi1234
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Client

Marken Fassi

Sector

Premium retail · Bedding

Timeline

3 meses · 2024-03

Live application

Open application

The challenge

Friday, 2 p.m. Ana, a sales associate at a multi-brand store in São Paulo, is helping a customer who asks for the softest bedding set for winter. For a moment she hesitates — there are dozens of products, yarns, weights and blends. Instead of calling the supervisor, she opens CASA FASSI. In 45 seconds, a micro-module reminds her of the fabric differences, suggests two options and links to a quiz she can finish before the customer tries the first piece. When the sale closes, the ticket tops R$ 1,800. Ana earns points, levels up and, next week, will be listed as a certified specialist.

O varejo premium de enxovais enfrentava vendedores de lojas multimarcas com conhecimento técnico limitado sobre tecidos, fios e diferenciais da marca, resultando em atendimento genérico e baixo ticket médio. Havia pouca retenção de conhecimento após treinamentos pontuais, ausência de conexão entre vendedores e a marca, dificuldade em escalar o relacionamento com centenas de lojistas espalhados pelo Brasil e falta de reconhecimento contínuo para a excelência em vendas.

The approach

We treated learning as a product, not content. First we mapped the salesperson's real journey: idle moments during shifts, quick customer interactions and decision points that define the ticket. From there we designed a microlearning architecture in 10 to 60 second bursts, punctuated by gamification, daily challenges and immediate feedback. The reward system was designed to compete with wellness apps: streaks, tiers, visual achievements and real benefits. AI enters as a coach at the exact moment the seller feels doubt, not as a replacement for the trainer.

The solution

CASA FASSI is a private Learning Experience Platform (LXP) for sales associates and retailers. It connects training, community, performance and recognition into an ecosystem where learning and selling happen in the same flow.

  • Microlearning in 10 to 60 second bursts, consumable between customers, with structured tracks by fabrics, collections and sales techniques — each module designed to fit the real idle window of the salesperson on the shop floor
  • Gamification with daily streaks, tiers from Bronze to Black Ruby, competitive leagues, achievement badges and real benefits like early access to collections and public peer recognition
  • FASSI.AI: sales assistant with a structured answer base from the catalog, brand script and sales context, designed to guide the seller's reasoning at the exact moment of doubt — with a roadmap toward LLM and RAG
  • Private community for sharing displays, sales cases, technical questions and best practices among salespeople from different stores, creating a peer-to-peer learning ecosystem
  • Performance dashboards so retailers can track teams, progress per track, certification rate, internal ranking and weekly engagement — with aggregated views by store and by seller
  • Certification program that positions salespeople as official brand specialists, with validated badges per completed track and proficiency levels recognized by Marken Fassi

Timeline

Como o projeto foi conduzido, do mapeamento ao deploy. How the project was conducted, from mapping to deploy.

Semana 1-2

Mapeamento

  • Immersion on the shop floor with sellers and retailers
  • Mapping the real journey between customer interactions
  • Identifying idle peaks and moments of doubt
  • Defining baseline metrics for retention and engagement

Semana 3-10

Desenvolvimento

  • LXP architecture with 10 to 60 second microlearning
  • Gamification implementation with tiers and streaks
  • FASSI.AI development with structured answer base
  • Weekly validation with real seller cohorts

Semana 11-12

Lançamento

  • Controlled launch with pilot retailers
  • Fine-tuning rewards and microcopy
  • Real-time KPI monitoring
  • Onboarding the first wave of certified sellers

Methodology

Como o projeto foi conduzido, do mapeamento ao deploy. How the project was conducted, from mapping to deploy.

1

Map the seller's real journey between customer interactions

2

Validate assumptions with retailers and floor salespeople

3

Document decisions in a shared decision log

4

Iterate weekly using retention and engagement metrics

5

Run A/B tests on microcopy, timing and rewards

6

Guided onboarding with pilot retailers before general launch

Architecture

Vendedores
mobile / web
CASA FASSI
Next.js 15 + Node.js
Lojistas
dashboard
PostgreSQL
Redis
OpenAI API
Workers
  • SSR container with Next.js 15 to deliver pages and cache
  • Serverless API for authentication, gamification and analytics
  • PostgreSQL container for users, retailers and progress data
  • Redis container for session cache, leaderboards and queues
  • OpenAI container to orchestrate RAG and response generation
  • Workers container for notification pipelines and async jobs

Conceptual diagram — does not reflect real implementation

Tech stack

Technical choices and what was left out.

Frontend

Next.js 15

Why: SSR, streaming and App Router to deliver lessons in milliseconds

Rejected alternative: Astro — strong for static content, but not for rich interactivity

React 19

Why: New hooks and concurrency for gamification animations

Rejected alternative: Vue 3 — mature, but less Server Components ecosystem

Tailwind CSS

Why: Fast visual standardization and a small CSS payload

Rejected alternative: Styled Components — adds runtime and increases bundle size

Backend

Next.js API Routes

Why: Serverless in the same repo, reducing latency and operations

Rejected alternative: Express on a VM — requires more infrastructure and has higher latency

tRPC

Why: Fully typed APIs, reducing errors between frontend and backend

Rejected alternative: Hand-written REST — more boilerplate and less type safety

Zustand

Why: Lightweight state management with local persistence for offline progress

Rejected alternative: Redux Toolkit — more boilerplate and a steeper learning curve

Data/Cache

PostgreSQL

Why: Consistent relational data for users, retailers and progress

Rejected alternative: MongoDB — flexible, but less predictable for joins and transactions

Redis

Why: Session cache, leaderboards and queues with fast data structures

Rejected alternative: Memcached — limited to simple key-value storage

Prisma

Why: Typed ORM and reliable migrations

Rejected alternative: Sequelize — less type safety and worse DX

AI/Infra

OpenAI

Why: Engine behind FASSI.AI for RAG and brand-toned response generation

Rejected alternative: Self-hosted open-source LLM — unacceptable infrastructure cost and latency

Vercel Edge Functions

Why: Low cold start and geo-distributed execution

Rejected alternative: AWS Lambda — higher cold starts and heavier configuration

Cloudflare Workers

Why: Workers for async pipelines and notifications

Rejected alternative: Long-running servers — high cost and waste for sporadic jobs

Decision log

What I chose, what I rejected, and why.

Microlearning instead of long courses

Why: The multi-brand salesperson has only a few idle minutes per day. 30-minute courses caused drop-off. 10 to 60 second fragments fit the real floor routine and double return frequency.

Rejected alternatives: We evaluated a traditional LMS and recorded videos, but historical adoption was low and knowledge retention was not measurable.

AI as coach, not an answer bot

Why: FASSI.AI was designed to guide reasoning: it asks, gives technical context and lets the salesperson answer. The current version uses a structured answer base from the catalog, with a roadmap toward LLM/RAG.

Rejected alternatives: A simple FAQ chatbot would give fast answers, but would not train the salesperson to argue and close with confidence.

Gamification with tangible benefits

Why: Points and leaderboards alone do not sustain long-term engagement. We linked tiers to prizes, public recognition and early access to collections, turning learning into career and status.

Rejected alternatives: We tested pure scoring and seasonal challenges, but without real rewards the retention curve dropped after the second month.

FASSI.AI

FASSI.AI — assistant design (planned RAG evolution)

1

Seller asks a question about a product or sales scenario

2

System identifies context: catalog, journey and seller history

3

Current version: structured answer base from catalog and brand script

4

Roadmap: RAG engine with LLM for generative responses in brand tone

5

Validation and guardrails before reaching the seller

Respostas validadas contra catálogo. Fallback para busca estática. Cache de 24h. Responses validated against catalog. Fallback to static search. 24h cache.

Gamification

Sistema de motivação estruturado para vendedores. Structured motivation system for sales teams.

XP

Leagues

Badges

Streaks

Leaderboards

Missions

Coins

Certifications

Gamification

Action
XP
League
Badge
Ranking
Reward
Motivation
New action

Security and access

Security and access governance

  • Distinct permissions for seller, retailer and admin roles

  • SSO for centralized login

  • Content moderation and reporting in the community

  • Data segmentation: each retailer only sees their own team

  • Secure integrations with catalog and gateways

  • LGPD compliance for handling seller data

Results

Client-reported results during rollout

28% Sales Increase

Baseline: 22%

Fonte: Marken Fassi sales report — Q2 2024

67% Daily Engagement

Baseline: 41%

Fonte: LXP analytics — 30-day average

42% Churn Reduction

Baseline: 18%

Fonte: Retailer retention — 6 months post-launch

R$ 1,850 Average Ticket

Baseline: R$ 1,440

Fonte: Transaction data — pre/post comparison

78% Certification Rate

Baseline: 31%

Fonte: CASA FASSI certification platform

2s FASSI.AI Response Time

Baseline: 8s

Fonte: Assistant latency metrics — 7-day average

Client-reported data

Lessons learned

Not everything went as planned.

The biggest lesson was that retention does not depend on more content, but on more context. Microlearning works when it is triggered at the exact moment of doubt. Gamification works when there is real status. And AI works when it amplifies the salesperson, not when it tries to replace them. I also learned that validating with real retailers in the second week saved months of adjustments.

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