QuikBot and EFGH bring real-time insurance to physical AI

QuikBot Technologies has teamed up with financial infrastructure company Embedded Financial Group Holdings (EFGH) to launch a real-time insurance model embedded directly into the infrastructure of physical AI systems.

As autonomous robots and last-mile delivery systems become more common in urban environments, organisations are grappling with the challenges of liability, safety, and accountability.

Traditional insurance models, typically built around static assets, are struggling to keep pace with dynamic systems that constantly interact with shared urban infrastructure, leading to fragmented systems and interoperability gaps, which can result in increased risks and uncertainties for both operators and users of these technologies.

To solve this problem, QuikBot is integrating EFGH’s insurance capabilities natively into its Ambient Permission Plane, an infrastructure layer designed to standardise identity, policy, and execution across robots, buildings, and urban environments.

The Singapore deep-tech startup operates a last-mile delivery service powered by a fleet comprising larger robots that consolidate orders, and smaller robots that use machine vision and Lidar (light detection and ranging) to navigate sidewalks. The robots can connect with building management systems to call for lifts and open security gantries.

“Physical AI is reaching a point where trust, not just intelligence, determines whether systems can scale,” said Alan Ng, founder and CEO of QuikBot Technologies, adding that by embedding insurance into physical AI infrastructure, every real-time operation, not just its static state, is authorised, accountable, and insured by design.

“This coverage includes cyber risks, public liability, personal injury, product defects, business interruption, and goods in transit,” he told Computer Weekly. “The key difference is that coverage is tied to each action. When a robot moves, accesses a building, or completes a task, that activity is recorded and insured in context. This will allow insurance to move with the machine.”

Embedding insurance directly into robotic systems also improves the claims process. While traditional claims rely on reconstruction – working backwards after an incident to figure out what happened – QuikBot’s platform maintains a complete, real-time record of permissions, location, and system status.

“If a robot enters a building and damages property, the platform holds a complete record of permissions, location, movement, and system status at that moment,” Ng said. “As the insurance is embedded, the claim can be triggered and verified directly from that data. As a result, what typically takes days can be reduced to minutes, and disputes are minimised because both sides are working off the same source of truth.”

For EFGH, the partnership marks a major step towards the “finternet”, an evolving concept focused on embedding financial services, payments, and insurance into the fabric of daily life and commerce.

“Autonomous systems are becoming a defining feature of how buildings, logistics networks, and cities operate, introducing a new class of risk that must be addressed intelligently,” said Dennis Ng, executive chairman of EFGH. “Our partnership with QuikBot enables us to embed protection directly into the infrastructure that governs these systems. This is embedded finance converging with physical AI, and it represents a powerful shift.”

QuikBot’s infrastructure is currently deployed across major developments in Singapore, including South Beach and Mapletree Business City. A new rollout is planned for the Punggol Digital District in May 2026, alongside ongoing expansions across Asia and into the Middle East, including Dubai.

Despite its potential for embedded insurance in robotic systems, Alan Ng noted that the model cannot yet be replicated for autonomous vehicles (AVs) on public roads, as the environments are vastly different today.

“QuikBot operates in controlled, permissioned environments where every interaction is tracked. Conversely, autonomous vehicles operate in open systems where regulation, liability, and data standards are still evolving,” he said.

For the model to extend to wider autonomous transport, Ng noted that three critical elements must mature: clear regulatory and liability frameworks; trusted, standardised real-time data; and deep, native integration between vehicles, urban infrastructure, and insurance systems.