PhilWeb Elects Conglomerate Leader Lance Y. Gokongwei as Chairman to Accelerate AI Infrastructure and Transnational Ecosystem Expansion

MANILA, Philippines, Aug. 28, 2026 /PRNewswire/ -- PhilWeb Corporation (PSE: WEB) today announced the election of prominent business leader Lance Y. Gokongwei as Chairman of the Board of Directors. 

This leadership transition completes the Company's institutional governance restructuring, following Gokongwei's recent ₱2.02 billion strategic equity investment and his appointment to the Board in July. The election signals PhilWeb's definitive shift from cross-border capital formation to the aggressive, large-scale execution of its commercial B2B AI-enabled technology roadmap. 

"This transition marks a critical inflection point for our capitalization and operational scale," said Chairman Lance Y. Gokongwei. "Our objective is clear: to deploy institutional-grade, AI-powered infrastructure that establishes a new operational standard for security and compliance across the regulated digital ecosystem." 

AI-Enabled Tech and Expanding Transnational B2B Strategy

Under Chairman Gokongwei's strategic oversight, PhilWeb is accelerating the deployment of its enterprise artificial intelligence layer. This technological pivot completes the Company's transition into an institutional-grade digital infrastructure provider capable of powering complex industrial integrations. 

The immediate operational mandate focuses on the integration of high-value backend capabilities:

• Real-Time Operational Monitoring
• Algorithmic Risk Management
• Regulatory Compliance Automation
• Anti-Fraud Detection
• Data-Driven Customer Lifecycle Optimization

President of PhilWeb, Brian Ng, stated: "Having Lance Gokongwei step into the Chairman role provides the institutional governance required for our next phase of scale. His strategic oversight ensures that our AI infrastructure rollout—specifically engineered to serve the complex backend needs of premium commercial tourism complexes and multinational platform providers—is executed with absolute capital market discipline." 

Building an Interconnected B2B Commercial Moat

The governance upgrade strengthens PhilWeb's position as the foundational operational backbone for the regulated digital technology sector. 

Currently, the Company's AI-driven technology powers a premier, interconnected ecosystem connecting Southeast Asia's top-tier commercial and cultural tourism conglomerates, multinational digital platforms, and licensed institutional partners. Furthermore, PhilWeb continues to embed elite global digital content and R&D institutions into its unified digital infrastructure, securing deep commercial moats and operational dominance in the B2B market. 

About PhilWeb Corporation (PSE:WEB)

PhilWeb Corporation is a Philippine Stock Exchange-listed technology company providing digital infrastructure, operational systems, and platform technology solutions supporting regulated digital ecosystems in the Philippines.

The Company focuses on scalable infrastructure, AI-enabled operational systems, compliance technologies, and platform solutions designed to support licensed operators and long-term ecosystem growth.

Investor Relations Contact:

Kenneth Ke
Group Investment Head
PhilWeb Corporation
Email: kenneth.ke@philweb.com.ph

Dian Agcapen
Investor Relations & Public Relations Director
PhilWeb Corporation
Email: apagcapen@philweb.com.ph

Media Relations Contact:

Arnel Vasquez
Media Relations Director
Rebel Marketing Philippines
Email: arnelvasquez@rebelmarketing.com.ph
Mobile: +63 917 584 3573

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PhilWeb Elects Conglomerate Leader Lance Y. Gokongwei as Chairman to Accelerate AI Infrastructure and Transnational Ecosystem Expansion

BEIJING, Aug. 28, 2026 /PRNewswire/ -- Amap, Alibaba's location-based services platform, today released ABot-Recon, a streaming 3D reconstruction model that requires only 12 consecutive frames to reconstruct scenes spanning over 10,000 frames in real time, eliminating the need for long-range memory.

The model achieves state-of-the-art accuracy on multiple public benchmarks — including KITTI, Oxford Spires and VBR — while reducing peak memory usage to roughly one-third that of comparable methods, making real-time 3D reconstruction feasible on consumer-grade hardware.

As autonomous driving and embodied AI systems move into open environments, they require continuous spatial understanding while in motion — knowing where they are, what surrounds them and where to go next. Conventional streaming 3D reconstruction systems maintain memory anchors that store and fuse historical information to preserve global consistency. As input sequences grow longer, such systems become progressively slower, less accurate and more memory-intensive.

ABot-Recon takes a fundamentally different approach. Rather than maintaining long-range memory anchors, the model operates within a fixed 12-frame local context window, predicting only the local point cloud and the relative pose between adjacent frames. An online composition mechanism then assembles the complete global trajectory incrementally, keeping computational complexity constant regardless of sequence length.

To address drift inherent in local prediction, ABot-Recon incorporates dedicated correction and constraint mechanisms at both the prediction and training stages, calibrating trajectory error in real time.

On the Oxford Spires long-sequence benchmark, ABot-Recon reduces average trajectory error by 40.6% compared with the prior leading method, achieving a relative rotation error (RPE-R) of 0.12 degrees — approximately 40% lower than the previous state of the art. On KITTI-02, the model achieves real-time reconstruction at 24.45 FPS, 1.24 times the speed of existing approaches, with peak memory usage of approximately 6.71 GB — meaning a consumer-grade GTX 1080 Ti is sufficient to run the full pipeline.

The model requires only monocular RGB video as input, and needs no depth sensors or pre-calibrated camera parameters. This positions ABot-Recon for deployment across private-area mapping, embodied AI training, autonomous driving and 3D content production — scenarios where pre-built maps are unavailable and real-time reconstruction is essential.

ABot-Recon's inference code, evaluation scripts and pre-trained weights are now open-sourced on GitHub. The project page is accessible at https://amap-cvlab.github.io/ABot-Recon-html/.

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Alibaba's Amap Introduces ABot-Recon, Reconstructing 10,000-Frame-Scale 3D Scenes From Just 12 Frames in Real Time