// CASE_STUDY_01

SALES
FORECAST
AI

ROLE
Tech Lead & Product Architect
CORE STACK
Python / React / AWS ECS / LLM
STATUS
PRODUCTION (Core)
// 01. THE_FRICTION

DATA LATENCY & FRAGILITY

"C-Level executives were making decisions based on 60-day-old data. The previous process involved manual extraction from ERPs into fragile Excel spreadsheets—a 2-month cycle prone to human error and manipulation. The goal was to eliminate the 'Spreadsheet Fatigue' and provide a single source of truth."

// 02. THE_ARCHITECTURE

DECOUPLED PREDICTION PIPELINE

[ ERP CLIENTE ] 
      |
      v
[ NORMALIZATION WORKER ] ---> [ AWS RDS (Unified Data) ] <--- [ ML ENGINE (ECS) ]
      (Python ETL)                    ^         |                     ^
                                      |         | (Writes Predictions)| (Reads History)
                                      |         +---------------------+
                                      |
                                [ PYTHON BFF ]
                                      |
                                      v
                             [ REACT FRONTEND ]

Architecture designed for read-performance. The ML Engine runs as an isolated service on ECS, ingesting historical data and persisting predictions back into the RDS. The Python BFF consumes this pre-computed data, ensuring zero-latency dashboards for the end-user.

// 03. R&D_INTELLIGENCE

NATURAL LANGUAGE INTERFACE

To democratize data access for non-technical directors, I developed a Text-to-SQL layer using LLMs. This allows executives to query complex datasets using plain Portuguese.

1

User asks question in natural language

2

LLM compares with Schema Metadata

3

System executes safe, readonly SQL

// USER_INPUT

"Qual foi o faturamento da região Sul em Janeiro?"

// SYSTEM_PROCESS

analyzing_schema...

mapping_entities [region, date, revenue]...

// GENERATED_SQL

SELECT sum(total_revenue)
FROM sales_transactions
WHERE region = 'SUL'
AND month = '01'
AND year = '2025';

// OUTPUT

R$ 353.220,00

// 04. UX_LEADERSHIP

TRUST-BASED VISUALIZATION

As UX Lead, I mentored the product design team to prioritize data legibility over aesthetics. We implemented clear confidence intervals and regional heatmaps to give directors the confidence to act.

PLACEHOLDER: MACRO_DASHBOARD_VIEW
FIG. 01 — MAIN VIEW
PLACEHOLDER: HEATMAP_COMPONENT
PLACEHOLDER: CONFIDENCE_INTERVAL_CHART
70%
COST REDUCTION

Eliminated the need for a dedicated data extraction squad, automating the entire ETL pipeline.

LIVE
DATA SYNC

Moved from semi-annual reports to real-time dashboards, enabling agile goal setting for all branches.

100%
PLUG & PLAY

Standardized data model allowed instant integration with new client ERPs without code changes.