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17–19 June 2025

Copenhagen

2025 Catalyst Projects

See Innovation Come to Life

At the heart of innovation at DTW Ignite, our 50+ Catalyst projects will debut their groundbreaking innovations live in the Quad and on the Innovation Arena stage.
Harnessing the collaborative global force of over 1000 industry minds from 250 organizations, our Catalyst project teams are pioneering solutions to directly impact TM Forum's Missions of AI & Data, Autonomous Networks, and Composable IT & Ecosystems to propel industry innovation and growth.
Experience first-hand their inventive and trailblazing demonstrations. Delve into the challenges tackled, use cases explored, and solutions forged. Connect with these visionaries to discover how you can leverage their achievements to align with your business objectives and advance future outcomes.

Browse Catalyst Projects

Agentic AI for customer-centric O&M

Agentic AI for customer-centric O&M

Telecom operators are racing to advance toward AN4-level autonomous networks, yet they face severe challenges: delayed user perception assurance, where passive manual responses fail to predict issues in advance, and slow complaint handling continues to erode customer satisfaction; imprecise fault analysis, making it difficult to reasonably prioritize critical issues amid redundant work orders; and inefficient fault handling, with low accuracy in root cause identification, siloed systems, and unvalidated solutions. These not only prolong the Mean Time to Repair (MTTR) but also threaten network stability. Addressing these challenges will deliver significant business value: drastically reducing operational costs by minimizing manual operations, enhancing network reliability through faster and more accurate fault resolution, and strengthening competitive advantages by upgrading service quality. For users, this means fewer network disruptions, quicker problem resolution, and consistently superior experiences—turning dissatisfaction into trust. This project integrates signaling analysis large models, spatiotemporal analysis large models, multi-agent collaboration, and digital twin technology, shifting the focus of operations from "network-centric" to "customer and business-centric." It enables proactive issue prevention, automated end-to-end cross-domain process closure, and risk-controllable solutions, reshaping the operation model to bring "ultimate user perception" to both operators and customers. The core value of operators' transition to L4 autonomous networks lies in achieving proactive and automated operations. However, the current passive, incident-driven O&M model keeps customer complaints high, with three key issues: 1. Lagging user perception assurance: Inadequate optimization of service quality improvement processes and weak ability to locate quality issues result in reactive operations that fail to "identify problems before users". Meanwhile, manual, lengthy complaint handling with bottlenecks leads to inefficiency and reduced customer satisfaction. 2. Inaccurate fault analysis: Massive alarms and work orders lack metrics for assessing impacts on services and user perception, treating all equally. The phenomenon of "one fault generating multiple orders" also exists, failing to prioritize critical fault handling. 3. Inefficient fault disposal: Low accuracy in root cause identification, over-reliance on expert experience for solutions, lack of automatic collaboration between cross-domain systems, and absence of simulation verification not only waste time and effort but also cause misjudgments. This leads to long Mean Time to Repair (MTTR), uncontrollable network operation risks, and impacts on network quality and customer service guarantee. These issues not only increase O&M costs but also directly affect customer retention. For instance, the churn rate of high-value users has risen year-on-year due to undetected service quality issues. Addressing these challenges will reshape the competitiveness of Communication Service Providers (CSPs): Signaling and spatiotemporal analysis models can enhance problem prediction accuracy; multi-agent collaboration enables "minute-level" closed-loop handling of cross-domain faults; digital twin verification reduces operational risks. For operators, users' demand for a "seamless network experience"—consistently stable and smooth service—has become core. Traditional "firefighting" O&M not only consumes resources but also erodes user trust, a fatal flaw in the digital era. For vertical industries like finance and healthcare, a more stable network will accelerate the implementation of their digital services, ultimately achieving value co-creation between CSPs and industry clients. This Catalyst project is part of the Innovate Asia 2025 AN Level 4 Moonshot Challenge

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URN: M25.5.868
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ODA monetization engine: Transforming data assets into business growth

ODA monetization engine: Transforming data assets into business growth

Introduction – ODA Monetization Engine Catalyst In an era where data is the new currency, communication service providers (CSPs) and enterprise verticals face mounting pressure to unlock the full value of their data assets. Despite generating vast volumes of operational, customer, and network data, most organizations struggle to monetize it effectively. Legacy systems, siloed architectures, and manual processes prevent real-time insight generation and hinder the adoption of advanced technologies like AI and digital twins. The ODA Monetization Engine Catalyst addresses this challenge by delivering a transformative platform built on TM Forum’s Open Digital Architecture (ODA). It enables CSPs and enterprises to transition from static data handlers to dynamic digital service orchestrators—unlocking new revenue streams, enabling autonomous operations, and accelerating innovation. The Problem Most CSPs operate in fragmented environments where data is disconnected from monetization opportunities. This results in: * Delayed time-to-market for new services * Limited ability to personalize offerings * Underutilization of AI and digital twin capabilities * Missed opportunities for partner-driven growth The Solution The ODA Monetization Engine is a modular, scalable platform that integrates: * Real-time data ingestion and enrichment to convert raw data into actionable insights * Analytics-as-a-Service for predictive intelligence across customer, network, and service domains * Decision Intelligence to dynamically segment users based on behavior and value potential * Partner-ready API exposure using TM Forum Open APIs for seamless integration * Flexible billing models supporting usage-based pricing, dynamic bundling, and real-time charging Impact This solution is not theoretical—it’s validated in production. Telkomsel, Indonesia’s leading mobile operator, deployed the ODA Monetization Engine and achieved: * A noticeable reduction in service launch cycles, enabling faster time-to-market * Significant new revenue generated from data-driven services and partner integrations * Steady monthly growth from API-based monetization and ecosystem expansion These results demonstrate how the Catalyst delivers real business value—transforming operations, accelerating innovation, and unlocking new monetization opportunities. Industry-Wide Applicability The platform extends beyond telecom, delivering value across verticals: * Governance & Public Sector: Smart city monetization, tourism optimization * Healthcare & Life Sciences: Predictive care models and insurance innovation * Transportation & Logistics: Autonomous supply chain monetization * Finance & Banking: Fraud detection and risk management * Retail & Commerce: Personalized marketing and customer engagement Why It’s a Breakthrough * First ODA-native monetization layer bridging architecture and business outcomes * Embeds AI-driven monetization directly into ODA components * Aligns with TM Forum’s Level-4 Autonomous Operations maturity * Proven at scale: managing 170M+ subscriber data streams By adopting this Catalyst, CSPs and enterprises gain a turnkey solution to transform data into growth—delivering measurable business impact, ecosystem expansion, and future-ready innovation.

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URN: C25.5.866
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InfraVerse: Breaking boundaries for XR sustainability

InfraVerse: Breaking boundaries for XR sustainability

Telecom infrastructure deployment—especially for rooftop, in-building, and dense urban environments—remains slow, costly, and error-prone. Existing planning methods rely on static blueprints, fragmented site data, and repeated site visits, leading to rework, delays, and missed revenue opportunities. The InfraVerse Catalyst addresses this bottleneck by applying telecom-specific building information modelling (BIM) to digitize the physical deployment process. It integrates drone imagery, AI-driven insight extraction, and genAI automation to transform how CSPs plan, validate, and deploy high-performance infrastructure—particularly in hard-to-serve, high-value areas. The Catalyst combines drone-based data collection, AI, and generative AI (genAI) with a telecom-specific BIM platform. Drones capture detailed visual data. AI then processes this data to extract structural, spatial, and environmental insights. Next, genAI generates critical documents—like EMF assessments, technical drawings, and permit applications—reducing manual work and speeding up compliance. This solution allows virtual site inspections, improves design accuracy, and reduces unnecessary travel. Teams collaborate more effectively using a unified digital model, streamlining deployment and cutting costs. CSPs can plan with greater precision, optimize equipment placement, and deliver stronger indoor and outdoor coverage. The system is scalable and sustainable - it enables energy-efficient design, lowers emissions, and helps meet green building standards. With fewer design errors and faster approvals, CSPs can deploy infrastructure faster, at lower cost, and with better quality control. The InfraVerse Catalyst helps CSPs break free from slow, reactive builds—replacing outdated planning with intelligent, digital-first workflows. The shift brings sharper accuracy, faster deployments, lower costs, and sustainability gains that can’t be ignored. It’s not just better planning—it’s a smarter path to market and a stronger, greener foundation for the networks of tomorrow.

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URN: C25.0.802
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Beyond Chatbots: Hybrid AI for fully automated proactive customer care - Phase II

Beyond Chatbots: Hybrid AI for fully automated proactive customer care - Phase II

Only 1 in 25 Unhappy Customers Will Complain The other 96% remain silent—leaving service providers with a costly blind spot. This silence leads to unresolved issues, missed opportunities for intervention, and ultimately, silent churn. For telecom operators, this means not just lost revenue but a failure to meet customer expectations. The Problem with Traditional Care Legacy care systems are reactive. They rely on customers to initiate the support journey—by calling, clicking, or complaining. Even with chatbots, dashboards, and predictive models in place, most tools operate in silos. They detect technical issues but rarely connect those issues to the specific customers affected, leaving care teams without the clarity or speed needed to respond effectively. The Shift to Proactive, Autonomous Care Phase One: Assisted Care In the beginning, AI played a supporting role in reactive care. Once a customer initiated contact—through a call, chat, or complaint—AI stepped in to predict intent, route queries, and prioritize responses. It helped optimize workflows, but only after the problem had surfaced. Phase Two: Autonomous, Proactive Engagement Now, we’ve flipped the model. When a service issue is detected in real time, the system identifies which customers have been affected. It then predicts how those customers are likely to respond—whether by calling support, submitting a complaint, or silently churning. Based on this prediction, the system proactively engages with each customer to address the issue before they take action. This Catalyst transforms telecom care from reactive support to proactive, intelligent engagement—delivered through a robust Hybrid AI approach built to scale across millions of interactions. AI at the Core of the Solution Built for telco, Designed for scale. * AI at the core – The operational engine, not a bolt-on * Closed-loop automation – From detection to resolution, no handoffs * Silo-breaking integration * ODA-aligned – Modular, open, and fast to deploy Business Impact Even in pilots delivered : * 30%+ improvement in service quality * 80%+ satisfaction in AI-led interactions * Reduced call volumes and churn * Increased upsell/cross-sell * Lower operational costs through automation The Result: Productivity at the core. Scale at the edge. Satisfaction across the journey.

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URN: C25.0.767
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OmniBOSS - Phase II

OmniBOSS - Phase II

OmniBOSS Phase II proves that even with minimal effort, agentic AI can provide contextual insights from real operational data, closing the gap between practice and execution while preserving telecom expertise for the future. OmniBOSS is an Agentic AI platform that revolutionizes how Communication Service Providers (CSPs) operate their B/OSS environments by embedding domain knowledge, best practices, and AI-driven oversight directly into operational workflows. Unlike traditional systems that passively store configurations and metrics, OmniBOSS proactively monitors, evaluates, and recommends corrective actions across B/OSS layers — acting as a real-time expert assistant. In Phase I, OmniBOSS demonstrated a working prototype of Agentic AI for B/OSS best practices using simulated data. The goal was to prove the conceptual feasibility: AI agents can understand, enforce, and recommend operational best practices across TM Forum-aligned domains like alarms, thresholds, and inventory. Phase II builds on this foundation by extending the solution in two key ways: 1. Real-World Data Validation We evolve from simulation to validation against real-world data samples (anonymized or exported from live systems). This elevates credibility by showing how agents respond to actual operational complexity, not just theoretical cases. 2. New Asset – Best Practice Coverage Heatmap We introduce a visual analytics layer that displays which TMF API areas are fully, partially, or not yet covered by best practice enforcement. This new asset acts as a strategic roadmap for CSPs to prioritize improvements and track operational maturity.

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URN: C25.5.888
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AN L4 Digital Twin ensures maximum service reliability

AN L4 Digital Twin ensures maximum service reliability

Service is revenue. Better service is better revenue. Reliable service is reliable revenue. High service reliability has always been the key to success for CSPs and the telco industry as a whole, yet even minor configuration errors can trigger network-wide failures, causing severe revenue losses. IP network failures often escalate to core service outages, while massive-scale device connections exacerbate operational complexity. Statistics reveal that 70% of global IP incidents stem from human configuration errors, exemplified by an operator making 6,000 annual manual changes with 10+ human-induced errors and outages yearly. Exponential growth in CSP network complexity drives hundreds of annual configuration changes, with single IP devices handling ~600K configuration lines. Manual analysis remains prevalent, causing inefficiency, human dependency, and most importantly it simply cannot fully mitigate the risks. Therefore, relying on human intervention is destined to become obsolete. With our solution, CSPs can pre-emptively identify misconfigurations and service impacts, eliminate human-induced network failures, have a much faster network change process, reduce reliance on 5+ year-experienced O&M staff. This alleviates executive concerns about network change accidents which has been a long-standing issue. Zero-Accident Guarantee: Pre-emptive identification of misconfigurations and service impacts, reducing network change risks. Intelligent Verification: Automated network-wide analysis of routing and traffic changes, replacing error-prone manual checks. Real-Time Emulation: A digital twin mirroring live networks to test changes virtually, eliminating the need for multi-day physical monitoring. Operational Efficiency: Accelerated testing/troubleshooting and reduced resource costs via lightweight, high-precision simulation. By shifting from reactive to proactive operations, this solution empowers CSPs to execute network changes confidently while safeguarding service continuity.

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URN: C25.0.828
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Agentic & autonomous AI for business excellence

Agentic & autonomous AI for business excellence

The traditional market for telecom operators is nearing saturation. However, with the rapid rise of AI technology, new opportunities in intelligent applications across industries are emerging. According to reports, the global smart applications market is projected to grow at a CAGR of 31.7% from 2025 to 2033, reaching $488.54 billion by 2033. To harness this opportunity, telecom operators should offer integrated computing and network services to support industrial AI applications, lowering the barriers and costs of these foundational resources and fostering their own “second growth curve.” However, operators face several issues when providing computing-network services. * First, the complexity of industry intelligent services requires operators to understand industry business characteristics, diverse resource characteristics, and integration of heterogeneous products / resources to meet customized B2B needs, leading to difficult business analysis and long configuration time. * Second, high AI resource costs—smart computing resources and networks for AI-driven applications are costly, and inefficient allocation prevents operators from achieving economies of scale. Additionally, the intensive computing nature of AI-driven applications greatly increases resource energy consumption and power costs. * Third, complex operation and maintenance (O&M) of heterogeneous resources across computing-network domains raises O&M difficulties and weakens service guarantee. In conclusion, ineffective operation means may cause operator margins to fall short of expectations. To address these challenges, this Catalyst project aims to build an intelligent infrastructure that integrates network and computing resources, enabling efficient deployment of vertical AI applications. By leveraging Agentic AI and autonomous operations, the project will enable intelligent understanding of industrial AI workloads, precise resource matching, dynamic scheduling, cross-domain operational assurance, and holistic energy-saving strategies. The project will focus on AI medical imaging in healthcare as the initial application scenario, lowering costs and barriers for hospitals and offering strong replicability for other AI healthcare use cases and cross-industry adoption, empowering telecom operators to explore new markets.

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URN: C25.0.816
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AN agent for 5G bearer networks

AN agent for 5G bearer networks

The 5G bearer network, which connects the 5G radio access network and the core network and supports high quality private line services, plays an extremely important role. But troubleshooting on the bearer network can be difficult. On one hand, alarms and faults frequently occur. For example, a broken optical cable broken may trigger hundreds of device alarms. On the other hand, it typically takes several hours for experts to complete a fault diagnosis and the on-site engineer often needs to contact the network operations center to obtain support. Yet during typhoons and other disasters, emergency relief and communication recovery must be completed quickly. This Catalyst is creating an intelligent fault management framework, encompassing network devices, the network management system (NMS) and the operations support system (OSS). The framework employs AI agents to automate the monitoring and diagnosis of root alarms, in place of manual operations, in common fault scenarios. In a scenario where a fault needs to be manually diagnosed, an AI copilot will provide support to the engineers via a natural language interface. A major step towards the development of a level four autonomous network, the end-to-end solution is based on a three-layer architecture that associates digital twins with AI foundation models. Drawing on embedded AI, the intelligent network element (NE) layer provides real-time awareness of the network status. The intelligent NMS layer enables self-closed-loop fault diagnosis in a single domain. Integrated with the NMS, the intelligent OSS layer can address fault scenarios across domains and vendors end-to-end. Having completed technical pre-research, the solution is being piloted by China Mobile Guangdong. After it is integrated into production, operations and maintenance in the province, the solution should greatly improve network stability and reliability, by reducing the time it takes to resolve faults. Improved data query efficiency and a more robust emergency response capability for natural disasters are also expected.

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URN: C25.0.848
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CX optimization via AI-driven SOC over autonomous networks

CX optimization via AI-driven SOC over autonomous networks

This Catalyst – championed by Claro Colombia, Claro Brasil, and Libyana Mobile – showcases how an AI-powered SOC can predict issues before customers notice, automatically resolve incidents, and align network performance with customer expectations. Built on TM Forum Open APIs, the architecture enables seamless interoperability, fast integration in brownfield environments, and real-time automation. It delivers a replicable framework – a modular, standards-based blueprint adaptable across operations, domains, and technologies. Designed for scalability, it accelerates autonomy through proven patterns and automation models. Why It Matters Telecom networks are more complex than ever, yet customers demand flawless connectivity and fast resolutions. Traditional NOCs fall short of managing customer experience. Frameworks like TM Forum’s Open APIs, Closed-Loop Automation, and MAMA highlight the need for integrated, real-time, customer-focused operations. A customer-centric SOC is key to resolving issues before they impact users, improving satisfaction, and reducing costs and churn. How It Works The SOC collects data from all layers of the network — access, transport, and core — and consolidates it through a cross-domain Data Mediation Layer capable of handling diverse protocols and systems. This enables a unified operational picture built from alarms, KPIs, logs, and real-time performance. But observability alone is not enough. The SOC enhances this picture with customer-centric signals — such as QoE, crowdsourced metrics, complaints, and churn risk — to understand not just what’s broken, but how it impacts the customer experience and the business. An AI/ML engine detects anomalies and predicts service degradations. When issues arise, an intent-based automation engine maps them to appropriate actions using TM Forum Open APIs, closing the loop with continuous validation. Key Benefits * Proactive Customer Assurance: Identify and resolve service issues before users experience them. By focusing on QoE and customer context, the SOC elevates satisfaction and reduces inbound complaints. * Faster Time to Resolution: Intent-based automation enables rapid remediation, decreasing MTTR and ensuring consistent service availability. * Reduced Churn Risk: With real-time insight into customer impact and contextual business signals, high-value accounts are protected from persistent quality issues. * Operational Efficiency: Automation handles routine tasks, allowing SOC teams to focus on strategy and innovation. The unified view improves collaboration across digital operations, network, and customer-facing teams. * Scalable, Reusable Architecture: Built on TM Forum Open APIs and aligned with the ODA and MAMA frameworks, the solution provides a blueprint for autonomous operations that can be replicated across CSPs. Expected Outcomes * Decrease in customer-initiated trouble tickets and care center interactions. * Improved perceived reliability, especially in high-ARPU segments. * Increased Net Promoter Scores as customer disruptions become rare and short-lived. * Lower operational costs through intelligent automation. * Industry-aligned reference architecture, validated by TM Forum's Value Operations Framework (VOF), and designed to accelerate autonomy adoption across telecom environments.

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URN: C25.0.806
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AI-driven sustainable connectivity: Cutting emissions for higher impact

AI-driven sustainable connectivity: Cutting emissions for higher impact

CSPs are expanding their digital infrastructure—and as they do, energy consumption continues to rise. This increases both operational costs and carbon emissions. This Catalyst addresses the urgent need to optimize energy use across network operations using AI, machine learning, and data analytics. The solution integrates an AI-driven analytics engine into the network management layer. It ingests telemetry data from power systems, equipment performance logs, and traffic patterns across both access and core domains. Using machine learning models, it detects anomalous energy use, forecasts load requirements, and dynamically adjusts network parameters—such as power modes, cooling profiles, and traffic routing—to minimize unnecessary consumption. These adjustments occur in near-real time, reducing operational overhead and extending the usable life of hardware. The solution follows TM Forum Open Digital Architecture (ODA) and Open APIs for modularity and interoperability. As a result, it supports modular deployment and seamless integration with existing OSS/BSS systems. Unlike traditional methods, this solution combines predictive analytics with intent-based automation. It supports smarter energy provisioning for both access and transport networks—reducing over-provisioning and unnecessary energy draw. CSPs gain a clearer view of their carbon footprint and can align energy-saving initiatives with broader sustainability targets. The business value is twofold: reduced energy and maintenance costs, and improved brand reputation through demonstrable sustainability leadership. In a market where environmental accountability increasingly influences customer and investor decisions, this Catalyst enables CSPs to lead by example. By embedding intelligent energy optimisation directly into operational workflows, CSPs can cut emissions, boost uptime, and reduce costs. Crucially, this Catalyst will demonstrate that they can do this without compromising performance. The aim is to show that sustainability isn’t just good governance—it’s a core driver of resilience and long-term growth.

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URN: C25.0.770
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Accelerating dynamic network marketplaces

Accelerating dynamic network marketplaces

As CSPs expand into hyperscaler, B2B and third-party markets, they are increasingly offering flexible network-as-a-service (NaaS) products and bundles. This Catalyst accelerates development of such network marketplaces by using CAMARA, TM Forum, and MEF APIs to streamline Multipoint VPN provisioning across multiple operators. This helps CSPs and hyperscalers rapidly launch multi-vendor services, cutting time-to-market and reducing costs through automated planning. In contrast to traditional manual methods, the APIs enable users to create and configure multi-site, multi-cloud network connections dynamically through a one-click marketplace interface. CSPs can thereby streamline the ordering and provisioning of network services. By orchestrating IP VPN connections across metropolitan and backbone networks, operators can easily deliver virtual private network services tailored to customer requirements. The integration of CAMARA, TMF and MEF APIs will facilitate end-to-end provisioning of VPN services across multiple operators, fostering seamless third-party marketplace integration. This capability empowers CSPs and hyperscalers to rapidly launch innovative multi-vendor services, dramatically reducing lead times from months to weeks. By enabling seamless marketplace integration, and abstracting network complexity through automated deployment processes powered by generative AI, CSPs can therefore efficiently manage and monetize their network assets. Key benefits include: 1. Projecting around 80% reduction in Quote to Deployment time. 2. Significant cost savings due to automated Order to Deployment, projecting up to 25% reductions in capital and operational expenses over five years 3. Upto 15% new revenue streams with the dynamic marketplaces. By adopting these APIs and secure integration frameworks, CSPs can significantly enhance competitiveness, driving customer acquisition as well as innovation and profitability. Industries benefiting from the enhanced network service automation made possible include finance, education, healthcare, IoT, cloud services, virtual conferencing, and more. Contributions to standards by TM Forum and the Linux Foundation Networking (LFN) through defining and orchestrating these APIs will then support wider adoption across the industry.

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URN: C25.0.836
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