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FinHarbor Unveils Payment Orchestration Module to Streamline Financial Operations

Nicosia, Cyprus – June 18, 2026 – FinHarbor, a provider of modular banking and crypto-acquiring infrastructure, has announced the launch of Money Flow, a new payment orchestration module designed to manage the entire lifecycle of transactions. The module aims to significantly reduce the time it takes for businesses to configure and modify payment processes involving fiat and cryptocurrency, moving from weeks to mere days, while incorporating built-in compliance reporting for every transaction.

Money Flow oversees deposits, withdrawals, transfers, and exchanges across both fiat and crypto networks. At its core, the module utilizes Temporal, a workflow engine engineered for long-running distributed processes. Each payment operation is treated as a stateful process that navigates through Anti-Money Laundering (AML) screening, ledger accounting, and final execution, whether within a traditional bank or on a blockchain. This stateful design ensures that operations can resume precisely where they left off, even if a service restarts mid-process or an external party experiences delays in responding. The system is also equipped to handle situations where a response is not received within a set timeframe, triggering compensating actions or escalating the issue to support, thereby preventing funds from being held in limbo.

The module promises to revolutionize the speed at which payment logic can be adjusted. Modifications that previously required approximately a week can now be implemented in about a day. Furthermore, new payment processes built upon existing integrations can be deployed within days. All flow logic is consolidated within a single service and adheres to a self-documenting code approach, eliminating the need for developers to scrutinize multiple subsystems to understand a specific operation.

This design also bridges the gap between finance leadership and engineering teams. Chief Financial Officers can articulate payment processes in business terms, which developers can then translate into code using a domain-specific language that remains accessible to non-technical stakeholders. This provides finance teams with direct insight into how money moves through the platform, rather than relying on indirect explanations of the underlying logic.

“Payment infrastructure has traditionally been a black box for the people who are ultimately accountable for the money inside it,” said Ilya Podoynitsyn, CEO of FinHarbor. “With Money Flow, a finance director can read the logic of a withdrawal or an exchange almost like a business document, request a change, and see it in production within a day. That changes the conversation between the finance function and engineering.”

Compliance is integrated as a dedicated layer within each workflow. AML rules can be configured by compliance officers or support staff through a visual constructor, depending on the team’s structure. For every payment, the system generates a report that can be shared with regulators or the company’s anti-money-laundering officer, providing licensed businesses with a documented audit trail for crypto-to-fiat operations.

Currently, Money Flow supports bank transfers, crypto withdrawals, payouts, pay-ins, exchanges, crypto and wire deposits, internal transfers, and administrative operations. The range of supported flows is expected to expand as new integrations are added.

“Most of the cost in payment systems comes from change, not from the original build,” Podoynitsyn added. “We designed Money Flow so that change becomes routine – retries, timeouts, and compensations are built into the engine, and teams spend their time on the logic of the business rather than on failure handling.”

The Money Flow module is available to FinHarbor clients as an integral part of the platform’s core infrastructure.

About FinHarbor

FinHarbor is a technical platform provider that enables the launch of compliant, modular financial products, ranging from digital wallets and neobanks to crypto ramps and over-the-counter (OTC) trading desks. Leveraging years of practical fintech experience, the platform encompasses onboarding, compliance, wallet management, transactions, card services, and reporting. It is delivered through a microservice-based architecture (ISO/PCI DSS-certified), a robust API layer, and offers deployment options that are either on-premise or cloud-ready. FinHarbor supports fiat-only, crypto-native, and hybrid business models across markets in Europe, the Middle East and North Africa (MENA), and beyond.

AI Agents Learn to Operate Physical Robots Autonomously

A fleet of eight robotic arms at Nvidia’s GEAR lab has spent the past few weeks independently mastering tasks such as inserting pins, seating graphics cards, and cutting zip ties. The only human involvement was in the subsequent documentation of the process.

This capability stems from ENPIRE, a framework detailed in a paper published Tuesday by researchers from Nvidia, Carnegie Mellon University, and UC Berkeley. ENPIRE delegates the entire robot training process to AI coding agents—the same software that already writes and tests its own code—allowing them to execute this training directly on physical hardware.

For the past year, coding agents like OpenAI’s Codex, Anthropic’s Claude Code, and Moonshot’s Kimi Code have been engaged in what researchers term “autoresearch,” a cycle of writing, testing, and rewriting code without human intervention. This loop has largely remained confined to digital environments, where resetting a failed experiment incurs no cost. ENPIRE, however, extends this process into the physical realm, where resetting an experiment necessitates the manipulation of actual robotic arms.

The system operates in two distinct stages. Initially, a human guides the agent in developing two essential tools: a reset routine to return the workspace to its initial state, and a reward function that analyzes camera footage to evaluate success. This setup, akin to a tireless, ever-vigilant referee, is established once and then reused for all subsequent attempts.

Once these foundational tools are in place, the AI agent assumes complete control. It scours published research for relevant ideas, selects training methodologies such as imitation learning, reinforcement learning, or predefined rules, and then modifies its own code before testing the outcome on the robot. This entire loop requires no human oversight, a development that could be seen as either liberating or slightly disquieting, depending on one’s comfort level with unsupervised robots wielding tools.

Nvidia conducted its experiment using eight bimanual robot stations, each equipped with its own hardware, computer, and coding agent. Progress across these stations is shared via Git, a collaborative coding tool, enabling successful strategies to be disseminated across the entire fleet within minutes.

The effectiveness of the system was measured using two tasks: “Push-T,” where a robot uses only pushes to slide a T-shaped block into a designated zone, and pin insertion, which involves threading pins into 4-millimeter holes. Scaling the operation from one robot to eight reduced the time required to master Push-T from approximately five hours to two, and pin insertion from over 90 minutes to about 40 minutes.

Across the four real-world tasks evaluated, the AI agents achieved a 99% success rate with their learned policies, according to the paper. For pin insertion, the agents reached near-perfect reliability faster than a comparable human-in-the-loop method, which typically requires daily human supervision.

Jim Fan, co-lead of the GEAR Lab and director of Nvidia’s AI research, described the project as the first effort to enable autoresearch in the physical world. Fan stated that the team provided the agents with a fleet of robots, GPU resources, and a token budget, then allowed the robots to autonomously complete the tasks.

Bridging the Simulation-Reality Gap

The disparity between simulated environments and the physical world became apparent almost immediately. While all three coding agents successfully completed the Push-T task within a simulator, two of them failed when the same task was transferred to a physical robot, the paper noted. This highlights the challenges posed by real-world factors such as friction, which are not accurately replicated in simulations.

Nvidia also tested ENPIRE within RoboCasa, a simulated kitchen environment designed to assess robots on chores like opening cabinets and turning off stoves. In this benchmark, ENPIRE surpassed both Nvidia’s own end-to-end model, GR00T, and CaP-X, a tool-using agent that bypasses the autoresearch loop entirely.

ENPIRE builds upon an idea Nvidia first introduced with Eureka in 2023, a system that utilized a language model to generate reward functions for robots within a simulator, eliminating the need for manual engineering. ENPIRE takes this self-improvement loop a step further by moving it from simulation to actual hardware, with the AI agent not only designing its own rewards but also its own testing procedures.

This development coincides with Alibaba’s recent unveiling of its own embodied AI initiative, the Qwen-Robot Suite, a collection of foundation models for robot navigation, manipulation, and physics simulation. While Alibaba is developing software for robots it does not manufacture, Nvidia is exploring the potential for AI agents to manage the entire research and development loop on its own hardware. Both approaches signal a growing trend: physical robots are emerging as the next frontier for AI coding agents to compete and innovate.

Allbirds, the footwear company once synonymous with its wool runners and a strong emphasis on sustainability, is pivoting entirely away from its original business. It is reinventing itself as an artificial intelligence infrastructure firm under a new banner: Smartbird.

The San Francisco-based company, which is publicly traded on Nasdaq under the ticker BIRD, announced Wednesday that it has finalized the sale of its Allbirds shoe and apparel operations. This move, initially disclosed in April, also saw the appointment of Nadia Carlsten, a seasoned executive from the AI and advanced computing sector, as its new president, CEO, and a member of its board.

Investors responded favorably to the latest developments. BIRD shares surged 52% on the day, reaching a recent price of $5.99. This follows a volatile period in April, when the stock saw a significant jump from $2.49 to a high of $24.31 before retracing most of those gains. Despite the fluctuations, BIRD shares remain up 46% year-to-date.

Carlsten joins Smartbird from DCAI, a company specializing in GPU compute infrastructure, where she previously served as CEO. In that role, she was instrumental in the launch of a sovereign AI supercomputer in collaboration with Nvidia. Her professional background also includes positions at Google spinoff SandboxAQ and Amazon Web Services, where she contributed to the introduction of Amazon’s quantum computing service. Carlsten holds degrees in chemistry and physics from the University of Virginia and an engineering doctorate from the University of California at Berkeley.

She succeeds Joe Vernachio, who is departing the company and its board. Lily Yan Hughes, an independent director, has been appointed board chair, while Annie Mitchell will continue in her role as chief financial officer. The company had previously considered rebranding as NewBird AI before ultimately selecting Smartbird.

“Smartbird is entering the market at a pivotal moment in the evolution of AI infrastructure,” Carlsten stated. “AI is rapidly becoming mission-critical for organizations across every industry, yet many organizations lack a practical path to deploy and operate the dedicated infrastructure these workloads require.”

She further elaborated, “There is a clear opportunity to meet the growing need for enterprise-grade AI infrastructure that delivers control and performance without the capital and operational burden of hardware ownership. With a differentiated strategy, significant capital, and the opportunity to build an exceptional team, we are uniquely positioned to capitalize on one of the most significant infrastructure opportunities of the next decade.”

As part of this strategic shift, the company has increased its convertible financing facility from $50 million to $100 million. This infusion of capital will enable Smartbird to develop what it describes as managed, dedicated AI computing clusters for its enterprise clients. The company indicated that it is actively engaged in discussions with potential customers and is currently designing its initial cluster deployments.