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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Examining the shift from fast answers to verified intelligence in enterprise AI.
Updated
January 8, 2026 6:33 PM
Startup employee reviewing business metrics on an AI-powered dashboard. PHOTO: FREEPIK
Neuron7.ai, a company that builds AI systems to help service teams resolve technical issues faster, has launched Neuro. It is a new kind of AI agent built for environments where accuracy matters more than speed. From manufacturing floors to hospital equipment rooms, Neuro is designed for situations where a wrong answer can halt operations.
What sets Neuro apart is its focus on reliability. Instead of relying solely on large language models that often produce confident but inaccurate responses, Neuro combines deterministic AI — which draws on verified, trusted data — with autonomous reasoning for more complex cases. This hybrid design helps the system provide context-aware resolutions without inventing answers or “hallucinating”, a common issue that has made many enterprises cautious about adopting agentic AI.
“Enterprise adoption of agentic AI has stalled despite massive vendor investment. Gartner predicts 40% of projects will be canceled by 2027 due to reliability concerns”, said Niken Patel, CEO and Co-Founder of Neuron7. “The root cause is hallucinations. In service operations, outcomes are binary. An issue is either resolved or it is not. Probabilistic AI that is right only 70% of the time fails 30% of your customers and that failure rate is unacceptable for mission-critical service”.
That concern shaped how Neuro was built. “We use deterministic guided fixes for known issues. No guessing, no hallucinations — and reserve autonomous AI reasoning for complex scenarios. What sets Neuro apart is knowing which mode to use. While competitors race to make agents more autonomous, we're focused on making service resolution more accurate and trusted”, Patel explained.
At the heart of Neuro is the Smart Resolution Hub, Neuron7’s central intelligence layer that consolidates service data, knowledge bases and troubleshooting workflows into one conversational experience. This means a technician can describe a problem — say, a diagnostic error in an MRI scanner — and Neuro can instantly generate a verified, step-by-step solution. If the problem hasn’t been encountered before, it can autonomously scan through thousands of internal and external data points to identify the most likely fix, all while maintaining traceability and compliance.
Neuro’s architecture also makes it practical for real-world use. It integrates seamlessly with enterprise systems such as Salesforce, Microsoft, ServiceNow and SAP, allowing companies to embed it within their existing support operations. Early users of Neuron7’s platform have reported measurable improvements — faster resolutions, higher customer satisfaction and reduced downtime — thanks to guided intelligence that scales expert-level problem solving across teams.
The timing of Neuro’s debut feels deliberate. As organizations look to move past the hype of generative AI, trust and accountability have become the new benchmarks. AI systems that can explain their reasoning and stay within verifiable boundaries are emerging as the next phase of enterprise adoption.
“The market has figured out how to build autonomous agents”, Patel said. “The unsolved problem is building accurate agents for contexts where errors have consequences. Neuro fills that gap”.
Neuron7 is building a system that knows its limits — one that reasons carefully, acts responsibly and earns trust where it matters most. In a space dominated by speculation, that discipline may well redefine what “intelligent” really means in enterprise AI.