AI in Brazil

Brazilian plant deployed AI-driven predictive maintenance across compressors, HVAC systems, and packaging equipment, achieving $2.3M annual savings on a $5.1M maintenance baseline, 40 percent reduction in unplanned downtime, and OEE above 85 percent for two consecutive years, the highest in Unilever's global manufacturing network.

Maristela Storti's corn farm in Jataí, Goiás, Brazil deployed Stenon's real-time soil analysis AI with Pioneer 3898 hybrid, achieving 7.98% higher yield, 20% less nitrogen use, and 0.14 tonnes/hectare lower CO2 emissions — a 17x ROI on the trial field.

Suzano developed VagaLumen platform using Google Cloud Cortex Framework and Gemini Pro 1.0 to transform natural language employee questions into SQL queries on BigQuery. Enables 50,000 employees to access SAP materials data through conversational AI, democratizing data access across the company while reducing manual spreadsheet work and improving data reliability for sustainable materials manageme

Creditas uses computer vision AI to assess the condition and value of collateral (vehicles, homes, and salaries) submitted for secured loans, enabling faster and more accurate credit decisions. For vehicle loans, the AI analyzes photographs submitted by customers to assess exterior and interior condition, detect prior damage, and verify vehicle authenticity, reducing the need for physical inspections by 85%. For home equity loans, the AI estimates property value using satellite imagery and comparable sales data. This AI-powered collateral assessment has enabled Creditas to offer interest rates up to 60% lower than traditional banks.

Vale uses machine learning-based predictive maintenance systems to monitor the health of critical mining equipment including excavators, conveyors, crushers, and drill rigs across its global operations. The AI system analyzes sensor data from over 100,000 monitoring points including vibration, temperature, pressure, and acoustic sensors to predict equipment failures before they occur.

Magazine Luiza's AI voice assistant Lu, available on the company's website and app, uses natural language processing to answer customer questions, provide product recommendations, and assist with order tracking and returns. Lu can understand natural Portuguese language including regional accents and colloquialisms, handling approximately 5 million customer interactions monthly with a 92% resolution rate.

Magazine Luiza has deployed AI across its logistics network to optimize warehousing, inventory allocation, and last-mile delivery routing. The system uses demand forecasting models to predict which products will sell in specific regions, enabling intelligent inventory pre-positioning across its 1,200+ stores that also serve as distribution points. Machine learning algorithms optimize delivery route sequencing for its own delivery fleet, reducing last-mile delivery time by 35% and vehicle kilometers traveled by 22%. The AI also predicts peak demand periods to optimize staffing at distribution centers.

iFood's recommendation system uses collaborative filtering and deep learning to personalize restaurant and menu item suggestions for each of its 50 million monthly active users. The AI considers user past orders, browsing behavior, time of day, location, weather conditions, and stated preferences to rank and display relevant restaurants and dishes. The recommendation engine has increased average order value by 18% and improved customer retention by 22%. iFood also uses AI to optimize restaurant positioning in search results, balancing relevance to individual users with fair exposure for restaurant partners.