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MetaComp Unveils Web2.5 VisionX Engine and AgentX, Advancing the StableX Network as the Compliance Layer for Agentic Payment and Wealth (1)
MetaComp's VisionX Engine lowers false clean rates to 0.24% using four blockchain analytics, enabling AI compliance governance for payments and wealth management.
- On Friday, March 27, 2026, Singapore-based MetaComp unveiled the Web2.5 VisionX Engine and AgentX, aiming to enhance compliance for institutional AI-native financial execution across fiat and digital asset networks.
- Systemic risk often exists at the handoff between traditional banking and blockchain, a gap previously unmonitored because no single tool could analyze both sides simultaneously until now.
- MetaComp's VisionX Engine operationalizes more than four blockchain analytics vendors in parallel, reducing false clean rates from up to 25 per cent to near-zero through proprietary aggregation.
- Alongside the engine, MetaComp introduced AgentX, an AI deployment layer, and announced Know Your Agent , a governance standard aligned with the Singapore Infocomm Media Development Authority .
- These advances are part of a progressive rollout through 2026 and 2027, supported by US$35 million raised across two Pre-A funding rounds to expand regulated payment corridors globally.
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MetaComp unveils Web2.5 VisionX Engine and AgentX, Advancing the StableX Network as the Compliance Layer for Agentic Payment and Wealth (1)
MetaComp unveils Web2.5 VisionX Engine and AgentX, Advancing the StableX Network as the Compliance Layer for Agentic Payment and Wealth (1)
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MetaComp Unveils VisionX and AgentX for StableX Network
MetaComp announced the rollout of its Web2.5 VisionX Engine and AgentX architecture, positioning the StableX Network as a compliance and execution layer for agentic payments and wealth services. The Singapore-based firm said the launch also includes development of a new “Know Your Agent” standard, or KYA, for AI-led financial activity. VisionX is built as a three-layer AML/CFT intelligence engine covering identity, behaviour, and network risk ac…
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Bias Distribution
- 43% of the sources lean Left, 43% of the sources lean Right
43% Right
L 43%
14%
R 43%
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