Brazilian agribusiness is data-intensive, from climate to logistics. At Lumini, we combine AI, automation, and data engineering to transform complex variables into practical decisions, with governance, security, and cost control.
Where does AI create value in the field
Crop forecast and productivity
- Models that combine production history, climate, soil, and management to estimate productivity by plot or region.
- Decision: harvest planning, purchase of inputs, and sales contracts.
- KPIs: forecast error (MAPE), yield per hectare, margin per crop.
- Technology base Data Science & AI
Pest and disease detection (computer vision)
- Classification from field images, drones, and satellites; early warnings and recommended actions.
- KPIs: response time, impacted area, treatment cost/ha, avoided loss.
- Orchestration and response routines with Austin – RPA + UX
Logistics optimization and routing
- Collection, distribution, and transfer route planning with real-world constraints (windows, capacity, roads).
- KPIs: kilometers traveled, cycle time, cost per ton/kilometer, emissions.
- Example: Reduction of up to 22% in the distance traveled in logistics using data science: Route Optimization Case
Predictive maintenance of machinery and fleets
- Analysis of telemetry, vibration, and failure history to predict breakdowns and plan shutdowns.
- KPIs: availability, MTBF, MTTR, maintenance cost per hour.
- Integrations and automations with Application Development
Traceability and quality (post-harvest)
- Consolidation of source data, storage and transport; classification and quality standards.
- KPIs: compliance, post-harvest losses, lead time, approved audits.
- Security and compliance Information Security
Knowledge Centers with RAG: Reliable Answers for the Field
- Connect manuals, agronomic protocols, crop history, and field records.
- Assistants that answer technical questions, recommend procedures, and generate reports.
- Start with Data Science & AI and orchestrate actions with Austin – RPA + UX
Data Architecture for Agriculture (Edge, Cloud, and Integrations)
- Edge + Cloud: collect data in remote areas with resilient synchronization.
- Lakehouse and catalogs: standardize, version, and qualify data from machines, sensors, and ERPs.
- APIs and connectors: integrate agricultural ERPs, telemetry, and field platforms.
- Observability and Quality: Automatic Metrics and Anomaly Alerts.
- Accelerate with Cloud AWS, Cloud Azure e Google Cloud
Governance, LGPD, and Sustainability (ESG)
- Privacy by design: producer data and geolocation with a legal basis and minimization.
- Operational safety: access segregation, records, and audits.
- Transparency: provenance and transformations for certifications and audits.
- Meet Information Security
Costs and FinOps: End-to-end predictability
- Drivers: bulk ingestion, storage, seasonal processing, and model usage.
- Best practices: data layers by cost, storage tiering, caching, and seasonal capacity adjustments.
- KPIs: Cost per hectare monitored, cost per ton transported, cost per automated decision.
- See our offer of FinOps
AI Diagnostics for Agribusiness (2 weeks)
Map high-impact cases (harvest, pests, logistics) and deliver a prioritized MVP.
KPIs by Use Case (Practical Examples)
- Safra: MAPE of forecast, productivity/ha, margin per crop.
- Pests/diseases: response time, area impacted, cost/ha, loss avoided.
- Logistics: km driven, cycle time, cost per ton, emissions.
- Maintenance: availability, MTBF, MTTR, cost per machine hour.
- Traceability: compliance, lead time, post-harvest losses, approved audits.
Common Mistakes (and How to Avoid Them)
- Starting too big: prioritize one culture, one region, and one measurable goal.
- Uncurated data: invest in quality and version control; document sources and policies.
- Lack of integration with operations: connect decisions to responsible systems and teams.
- Ignore cost seasonality: adjust capacity and budget by harvest.
- Reactive security: policies and audits from the first MVP.
30-60-90 Plan for Agriculture
- 30 days — Discovery and data: define use cases, KPIs, and policies; run initial ingestion and baseline models.
- 60 days — Field-oriented PoC: deploy pilots with operations teams; track metrics and costs.
- 90 days — Scale and automation: orchestrate routines with RPA/Agents; quality and cost dashboards; team training.
How Lumini helps (from diagnosis to operation)
- Forecasting Models and Computer Vision Data Science & AI
- Field automation and orchestration: Austin – RPA + UX
- Integrations, APIs, and field apps: Application Development
- Security, LGPD, and Compliance: Information Security
- Tailored cloud and cost optimization: FinOps
Talk to Lumini
Want to take AI from the lab to the field with predictable ROI?
