Ant International’s Antom has introduced significant upgrades to its Antom Copilot, an AI agent developed to manage merchant payment and operations.

The upgraded agent is now integrated in the Antom Merchant Portal, offering automation and customised support informed by real-world scenarios to aid merchants in navigating complex payment processes.

The features of Antom Copilot include delivering agentic support throughout the payment lifecycle, including onboarding, integration, dispute resolution, risk management, and payment success rate operations.

It incorporates domain-specific intelligence features, functioning as a virtual team of payment specialists to handle processes that have traditionally required substantial resources, aiming to reduce both processing time and costs.

The features also include demonstrating performance in resolving issues within multi-step operational environments, backed by enhanced multi-turn dialogue and memory offerings.

Additionally, the agent enables closer operational signal monitoring, allowing for the proactive identification of risks and opportunities before merchants report them.

Antom Copilot is said to operate on an upgraded agentic retrieval-augmented generation (RAG) architecture, featuring dynamic standard operating procedures (SOP) planning.

According to Antom, its AI agent has decreased merchant payment integration duration by 90% and increased dispute-handling efficiency by 46%.

In addition to these upgrades, Antom has released EPOS360, an AI-based application that consolidates point-of-sale (POS) systems, banking, payments, lending, and growth support to assist small and medium-sized enterprises in scaling their operations.

Antom Copilot is part of Ant International’s broader strategy to progress agentic payment technologies and support the development of AI-powered commerce.

Ant International chief innovation officer Jiangming Yang said: “Ant International is focusing on building domain-specific agentic AI that solves global payment complexity for merchants.

“We train and tune AI agents using deep industry know-how and a large set of anonymised complex cases, and evolve new agent protocols to ensure agents solve tasks in efficient orchestration.”