GIBO.ai Enabled Industry and Energy Insights by Turning Aerial Mobility Data into AI Intelligence

GIBO.ai Enabled Industry and Energy Insights by Turning Aerial Mobility Data into AI Intelligence

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GIBO Holdings Ltd. (NASDAQ: GIBO), Asia's leading innovation-driven AI ecosystem, announced the next phase of its AI aviation roadmap, extending the GIBO.ai Calculation Engine beyond flight operations to deliver AI-powered aerial intelligence and data analytics services for commercial, industrial, and sustainability-focused markets.

Building on its expansion into scalable AI-powered eVTOL platforms, GIBO is now positioning aerial systems as data-generating intelligence assets, capable of producing high-value insights that support infrastructure planning, environmental assessment, energy optimization, and ESG-driven decision-making.

From Flight Platforms to Intelligence Services

Modern eVTOL aircraft generate vast volumes of real-time data, including environmental sensing, terrain imaging, flight dynamics, and operational telemetry. Through the GIBO.ai Calculation Engine, this raw data is transformed into structured, actionable intelligence that can be applied across multiple industries.

Rather than treating aerial vehicles solely as mobility solutions, GIBO.ai enables them to function as intelligent data nodes, continuously capturing and analyzing information from the physical world. This shift allows aerial operations to deliver ongoing analytical value long after each mission is completed.

AI Calculation Converts Aerial Data into Actionable Insights

The GIBO.ai Calculation Engine applies advanced AI models to interpret aerial data streams, enabling precise analysis of terrain conditions, infrastructure integrity, environmental patterns, and operational risk. By combining real-time computation with post-mission analytics, the system produces insights that are both immediate and longitudinal.

These AI-calculated outputs can support use cases such as infrastructure inspection, energy asset monitoring, geological assessment, environmental impact analysis, and site-planning optimization. As a result, enterprises gain deeper visibility into physical environments that are traditionally difficult, costly, or time-consuming to assess.

Supporting Sustainability, ESG, and Green Economy Applications

A core focus of this initiative is the application of AI-derived aerial intelligence to sustainability and environmental performance measurement. GIBO.ai enables organizations to quantify environmental conditions, monitor change over time, and validate operational impact with data-driven confidence.

By converting aerial observations into verifiable datasets, the platform supports ESG reporting, sustainability benchmarking, and compliance with emerging environmental standards. This positions GIBO.ai as a foundational intelligence layer for companies seeking to integrate environmental accountability into their operations.

"The true value of AI-powered aviation lies in the intelligence it produces."

"Aerial platforms are becoming one of the most efficient ways to observe and understand the physical world," said Zelt Kueh, CEO of GIBO Holdings Ltd.
"With the GIBO.ai Calculation Engine, flight data evolves into intelligence services that industries can rely on for planning, optimization, and sustainability. This is where AI moves beyond mobility and becomes a decision-making engine for the green economy."

A Scalable Intelligence Model for Multiple Industries

The aerial intelligence services enabled by GIBO.ai are designed to scale across sectors, supporting energy infrastructure, environmental services, logistics planning, smart-city development, and future mobility ecosystems. As data accumulates over time, the AI models continue to learn, improving accuracy, predictive capability, and long-term value.

This data-centric approach complements GIBO's broader vision of integrating AI across air and ground mobility systems, enabling a unified intelligence framework that connects transportation, infrastructure, and sustainability analytics.