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Unplanned downtime costs LATAM refineries hundreds of thousands of dollars per hour. Yet, 85% of operators still rely on manual inspections that take weeks to complete and often miss early warning signs. The result? Failures discovered too late, emergency shutdowns, and spiraling operational costs.
When a compressor fails unexpectedly in Mexico or Brazil, it’s not just a maintenance ticket—it’s half a million dollars lost in a matter of hours. The stakes for moving from reactive to predictive maintenance couldn’t be clearer.
The Predictive Shift
Instead of sending inspectors into hazardous zones with clipboards and SD cards, autonomous drone-in-a-box systems capture high-resolution data on flare stacks, tanks, and pipe racks daily. Edge AI modules process imagery on-site, flagging corrosion, leaks, or overheating in real time—reducing streaming costs by up to 5×. Insights feed directly into existing asset management systems, so maintenance teams act before failures occur.
Proof from LATAM
- Pampa Energía (Argentina): As demonstrated in our thermal power plant deployment, autonomous drones have already shortened inspection cycles and reduced risk in energy operations across the region.
- Anglo American (Peru): Mining leaders like Anglo American in Peru are already scaling predictive workflows with FlytBase-enabled automation, proving the model works at tier-1 scale.
- Regional Partners: UAV Latam, Drone Store Chile, Runco and Walross are helping operators deploy predictive monitoring tailored to LATAM realities.
These projects show predictive AI isn’t a distant vision—it’s delivering results across LATAM energy and mining. Refineries are the natural next step.
What FlytBase and Verkos Delivers
FlytBase’s Verkos AI platform is designed to make predictive operations real for refineries:
- Predictive intelligence: AI detects corrosion, dust contamination, and equipment degradation before they cause downtime.
- Compliance automation: Reports generated in minutes, aligned with API 6A and 14C standards.
- Seamless integration: Works with existing CCTV, IoT sensors, and enterprise systems—no rip-and-replace. For refinery-specific workflows, see our oil & gas solutions.
- Cost efficiency: Edge AI processes video locally, cutting OPEX while still delivering real-time alerts.
ROI for Refinery Leaders
Early deployments across LATAM energy operations show:
- Downtime reduction: Up to 30% fewer outages.
- Inspection savings: 40–60% lower inspection costs.
- Speed: 30× faster reporting (10 minutes vs 5 hours).
- Safety: Fewer worker hours in hazardous zones like flare tips and tank farms.
These aren’t theoretical gains—they’re measurable outcomes from organizations already running predictive workflows.
Take Action
Competitors in mining and energy across LATAM are already proving predictive monitoring works. Refineries that delay risk falling behind in safety, efficiency, and cost control.
Book a private demo of Verkos AI and benchmark your operations against Anglo and Pampa Energía’s predictive deployments. See firsthand how autonomous monitoring can reduce inspection time by 90% and prevent costly shutdowns.
FAQs
Find quick answers to common questions about compatibility, setup, features, and pricing
Because downtime costs are massive—hundreds of thousands of dollars per hour. Manual inspections are slow, risky, and often miss early failure signs. Predictive monitoring detects issues before they escalate, reducing unplanned outages by up to 30%.
Most drone programs need pilots on site. FlytBase enables autonomous, docked drones that launch, inspect, and land without human intervention. Edge AI processes the data locally, sending only actionable alerts—cutting both risk and streaming costs.
Refineries typically begin with high-value, high-risk assets: flare stacks, tank farms, pipe racks, and compressors. These areas see the fastest ROI because predictive alerts prevent shutdowns in critical units.
Yes. Pampa Energía in Argentina shortened inspection cycles with autonomous drones. Anglo American in Peru scaled predictive workflows in mining. These successes prove the model is ready for refinery operations.
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