Inside Mercuryo’s Visa Partnership
Updated
January 29, 2026 1:34 PM
Close up of Visa credit cards. PHOTO: ADOBE STOCK
Mercuryo is a fintech startup that builds the infrastructure to enable money to move seamlessly between crypto and traditional banking systems. In simple terms, it works on the problem of turning digital assets into usable cash.
As more people hold crypto through wallets and exchanges, one practical issue keeps arising: how do you actually withdraw that money and use it in the real world? For many users, converting tokens into local currency is still slow, confusing or expensive. That gap between “owning” crypto and being able to spend it is where Mercuryo operates.
The company’s latest step forward is a partnership with Visa to improve what is known as “off-ramping” — the process of converting crypto into fiat currency like dollars or euros. Until now, this has often been slow, expensive and confusing for users. Mercuryo is using Visa Direct, Visa’s real-time payments system, to make that process faster and more direct.
With this integration, users can convert their digital tokens into local currency and send the money straight to a Visa debit or credit card. The transaction happens through systems that already power global card payments, which means the money can arrive in near real time instead of days later.
Technically, this connects two very different worlds. On one side is blockchain-based crypto, which moves value on decentralised networks. On the other side is the traditional payment system, which runs on banks, cards and regulated rails. Mercuryo’s platform sits between the two and handles the conversion and movement of funds.
Instead of users leaving their wallet or exchange to cash out, Mercuryo allows the conversion to happen inside the apps and platforms they already use. The user does not need to understand the plumbing behind it. They just see that crypto becomes spendable money on their card.
This matters because access is what makes any financial system usable. If people cannot easily move their money, they treat it as locked or risky. Faster off-ramps make digital assets more practical, not just speculative.
Mercuryo’s work is not about creating new tokens or trading tools. It is about building the pipes that let money move smoothly between Web3 and the traditional financial world. The Visa partnership strengthens those pipes by using a global, trusted payments network that already works at scale.
Visa also framed the partnership as a bridge between systems. Anastasia Serikova, Head of Visa Direct, Europe, said: "By leveraging Visa Direct's capabilities, Mercuryo is not only making converting to fiat faster, simpler and more accessible than ever—it's building bridges between the crypto space and the traditional financial system. This integration empowers users to seamlessly convert digital assets into fiat in near real time, creating a more connected and convenient payment experience".
Over time, this kind of infrastructure is what determines whether crypto remains niche or becomes part of everyday finance. Not through headlines, but through systems that quietly reduce friction.
Mercuryo’s direction is clear: make digital assets easier to use, easier to exit and easier to connect to the money systems people already rely on.
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Robots that learn on the job: AgiBot tests reinforcement learning in real-world manufacturing.
Updated
January 8, 2026 6:34 PM
A humanoid robot works on a factory line, showcasing advanced automation in real-world production. PHOTO: AGIBOT
Shanghai-based robotics firm AgiBot has taken a major step toward bringing artificial intelligence into real manufacturing. The company announced that its Real-World Reinforcement Learning (RW-RL) system has been successfully deployed on a pilot production line run in partnership with Longcheer Technology. It marks one of the first real applications of reinforcement learning in industrial robotics.
The project represents a key shift in factory automation. For years, precision manufacturing has relied on rigid setups: robots that need custom fixtures, intricate programming and long calibration cycles. Even newer systems combining vision and force control often struggle with slow deployment and complex maintenance. AgiBot’s system aims to change that by letting robots learn and adapt on the job, reducing the need for extensive tuning or manual reconfiguration.
The RW-RL setup allows a robot to pick up new tasks within minutes rather than weeks. Once trained, the system can automatically adjust to variations, such as changes in part placement or size tolerance, maintaining steady performance throughout long operations. When production lines switch models or products, only minor hardware tweaks are needed. This flexibility could significantly cut downtime and setup costs in industries where rapid product turnover is common.
The system’s main strengths lie in faster deployment, high adaptability and easier reconfiguration. In practice, robots can be retrained quickly for new tasks without needing new fixtures or tools — a long-standing obstacle in consumer electronics production. The platform also works reliably across different factory layouts, showing potential for broader use in complex or varied manufacturing environments.
Beyond its technical claims, the milestone demonstrates a deeper convergence between algorithmic intelligence and mechanical motion.Instead of being tested only in the lab, AgiBot’s system was tried in real factory settings, showing it can perform reliably outside research conditions.
This progress builds on years of reinforcement learning research, which has gradually pushed AI toward greater stability and real-world usability. AgiBot’s Chief Scientist Dr. Jianlan Luo and his team have been at the forefront of that effort, refining algorithms capable of reliable performance on physical machines. Their work now underpins a production-ready platform that blends adaptive learning with precision motion control — turning what was once a research goal into a working industrial solution.
Looking forward, the two companies plan to extend the approach to other manufacturing areas, including consumer electronics and automotive components. They also aim to develop modular robot systems that can integrate smoothly with existing production setups.