2025 Catalyst Projects

See innovation come to life

At the heart of innovation at Innovate Asia, 15+ Catalyst projects will debut their groundbreaking innovations live in the expo hall and on the Innovate stage.

Harnessing the collaborative global force of the greatest industry minds from global organizations, our Catalyst project teams will demonstrate their proof-of-concept solutions. Connect with these visionaries to discover how you can leverage their achievements to align with your business objectives and advance future outcomes.

Make sure to add these Catalysts sessions to your agenda:

25 November | 12:30 PM - 1:20 PM: Catalyst Spotlight: Shaping the Future of Autonomous Networks
25 November | 2:30 PM - 3:10 PM: Catalyst Spotlight: Transformation with AI and Data Innovation
25 November | 3:10 PM - 4:00 PM: Catalyst Spotlight: The Power of Composable IT and Ecosystem
26 November | 12:00 PM - 12:40 PM: Catalyst Moonshot Spotlight: AN Level 4 Challenge
26 November | 12:40 PM - 1:10 PM: Catalyst Moonshot Panel: AN Level 4 Challenge

Catalyst Champions include:

Browse Catalyst Projects

ODA prism: Recommending the ultimate telecom plan - Phase II

Phase II of this Catalyst advances our mission to help telecom providers thrive in an increasingly competitive and fast-moving wireless market. As customer expectations rise and competitive offerings shift rapidly, operators struggle with limited insight, siloed processes, high churn, and slow time-to-market for new plans. It tackles those challenges head-on by delivering a real-time, intelligence-driven solution that personalizes the customer journey and enables operators to recommend the “ultimate plan” for each customer—every time. This phase extends the solution with a more scalable AI-driven architecture aligned to TM Forum’s ODA and Open APIs. The enhanced design supports additional business use cases, strengthens interoperability, and introduces next-best-action capabilities that benefit both telecom providers and their customers. At its core, ODA prism unifies disconnected value streams through a catalog-driven, P-S-R-aligned architecture and advanced customer intelligence. By combining real-time decisioning, journey orchestration, and Product-Based modeling, the solution predicts churn, identifies key moments for upsell and cross-sell, accelerates offer launch, and ensures internal and external compliance for rapid go-to-market execution. Gen-AI and Agentic AI further enhance decision accuracy with domain-specific reasoning and generative insights. Proven use cases—including personalized acquisition, price and plan optimization, and proactive churn management—demonstrate how the solution boosts customer satisfaction, increases ARPU, and strengthens lifetime value. Success will be measured by reductions in churn, increased revenue per customer, improved personalization across value chains, operational efficiencies, and dramatically faster speed to market. Ultimately, ODA prism equips operators with the intelligence and agility required to meet evolving customer needs, outperform competitors, and drive sustained business growth in the next era of wireless communications.

URN: C26.0.917

Project details

NPS enables best network and customer experience

Net Promoter Score (NPS) is widely used by telecom operators as a top-level executive metric for measuring customer loyalty, yet the industry still relies largely on survey-based, reactive approaches that fail to scale across millions of subscribers. As a result, CSPs struggle to translate NPS insights into timely actions that reduce churn, improve customer experience, and increase lifetime value. The GenAI Predictive NPS Enable Best Customer Experience introduces a data- and AI-driven framework to manage NPS holistically across the customer lifecycle. Instead of treating NPS as a periodic survey result, the solution models NPS continuously using multi-source operational, behavioral, and service data, enabling proactive identification of experience risks and “off-grid” customers who are likely to disengage without submitting complaints. Building on proven deployments, the Catalyst demonstrates how predictive NPS (P-NPS) models can outperform traditional approaches by accurately identifying high-risk customers and prioritizing retention actions that deliver measurable business value. The solution focuses on practical scalability—optimizing data quality, improving model accuracy, and defining a minimum, cost-effective feature set that balances predictive performance with operational feasibility. Aligned with TM Forum best practices, the Catalyst enables CSPs to connect network performance, service quality, and customer behavior into a unified NPS management loop. Success is measured through improved model accuracy, reduced churn, and increased customer lifetime value, with even modest NPS improvements translating into millions of dollars in retained revenue for large operators. By transforming NPS from a static score into an actionable, AI-driven decision engine, this Catalyst provides CSPs with a repeatable, industry-relevant approach to delivering better network experiences, stronger customer loyalty, and sustainable business growth.

URN: C26.0.974

Project details

Conflict management in intent-based networks - Phase II

As networks evolve toward autonomous, intent-driven operation, Communications Service Providers face a critical challenge: how to detect, analyse, and resolve conflicts between multiple autonomous entities acting simultaneously. These conflicts—arising from competing intents, overlapping policies, or resource contention—can lead to service degradation, instability, or unintended behaviour if not managed proactively. The Conflict Management in Intent-Based Networks Catalyst project demonstrates a framework for ensuring safe, predictable, and coordinated operation across autonomous network functions. Building on previous Catalyst work in end-to-end intent-based service realisation, this project shows how CSPs can introduce automated conflict detection, classification, and resolution mechanisms into their intent-driven architectures. The solution uses autonomous engines to evaluate intents, analyse the impact of policy changes, and identify conflicting actions across layers and domains. It then applies rule-based and AI-driven strategies to propose or execute optimal resolutions, maintaining service quality and intent compliance even in complex multi-agent environments. By addressing one of the biggest operational risks in autonomous networks, this Catalyst equips CSPs with the tools needed to adopt intent-based operations confidently and safely, accelerating the journey toward fully autonomous networks while protecting customer experience and network integrity.

URN: C26.0.930

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Inter-operator composability for emergency response: OmniBOSS - Phase III

When disaster strikes, communications infrastructure is often the first critical system to fail—and the last to be fully restored. Today’s telecom networks are built with redundancy across systems, domains, and operators. Yet when large-scale disruptions occur, operators still act independently, restoring services independently, causing delays when every minute matters. With OmniBOSS Phase III we challenge this model. In response to the devastating impact of Cyclone Ditwah in Sri Lanka—where widespread flooding and landslides caused severe telecom outages and delayed relief efforts—we explore a different approach: What if operators could dynamically compose networks across organisational boundaries—sharing infrastructure, systems, and resources in real time? From Redundancy to Composability We want to demonstrate that inter-operator network composability can fundamentally change how the industry responds to crises. Enabling operators to combine infrastructure, coordinate field teams, and orchestrate systems across boundaries makes it possible to * Restore life-saving connectivity within the critical search-and-rescue window, dramatically reducing time to reconnect affected areas * Reactivate communication infrastructure rapidly, combining network capabilities across operators when every minute matters * Enable real-time, cross-operator emergency response, allowing networks, resources, and field forces to act as one * Prioritise and protect critical communications, ensuring continuity for emergency services, healthcare, government, and security operations * Maintain access to essential services, safeguarding critical digital platforms such as mobile financial services (e.g. M-PESA, bKash) * Coordinate recovery efforts more efficiently, improving logistics and making field operations far more targeted Delivered through * A geofenced digital twin simulating disaster impact * A governance and policy framework ensuring secure, controlled collaboration * Real-time orchestration across networks and operators And enabled via a distributed AI platform, coordinating response and field activity. Collaboration by Design Cross-operator collaboration must be trusted, secure, and commercially viable, ensured through * Policy-driven data sharing with strict governance controls * Geofenced and abstracted data models to protect competitive boundaries * Full auditability of actions and decisions * Transparent cost allocation and automated settlement across operators This enables collaboration that is not only technically feasible—but operationally and commercially sustainable Standards-Driven, Real-World Execution OmniBOSS Phase III extends TM Forum standards into real-time, multi-operator coordination, built on: * 15+ TM Forum Open APIs * eTOM-aligned processes * SID-based data models With contributions in: * Emergency Coordination APIs * Policy & Trust frameworks for composability * Real-time event collaboration Bridging the gap between standardisation and live execution. Key Benefits at a Glance * Restore life-saving connectivity faster during the critical rescue window * Enable real-time cross-operator coordination of networks and field force * Protect critical communications through policy-driven prioritisation * Maintain essential services for affected populations * Support governed collaboration with full transparency, control, and settlement Turning weeks of disruption into days—and days into hours.

URN: C26.0.954

Project details

Robotic dog: AI at the edge, sustainable revenue at scale

As intelligent devices become woven into daily life, there is a profound opportunity to use network-driven AI to empower individuals with visual impairments. The Reimagined Independence for the Visually Impaired Catalyst explores how communication networks, sensing data, and real-time intelligence can work together to enhance personal autonomy, safety, and confidence for those navigating complex environments. Using a real-life scenario as its foundation, the project demonstrates how network intelligence can interpret surroundings, identify obstacles, and deliver context-aware guidance to visually impaired users. By integrating device-level sensing with telecom network capabilities, the solution provides timely, accurate support that adapts to a person’s journey, environment, and needs. This Catalyst highlights how CSPs can extend their role beyond connectivity—creating meaningful societal impact through inclusive technology. It shows the potential for network-assisted guidance systems to reduce barriers, improve accessibility, and enable millions of visually impaired individuals to move more independently through the world. By blending human-centric design with advanced AI and network intelligence, the project illustrates a future where telecom innovation directly enhances quality of life and supports a more accessible digital society.

URN: C26.0.942

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The butler did it: Turning telco infrastructure into AI services

This Catalyst demonstrates how communications service providers (CSPs) can transform their network, edge, and compute assets into AI-native, outcome-driven services through XAaS (X Agents as a Service). Instead of selling undifferentiated connectivity, CSPs expose “Butlers” — autonomous, agentic services that deliver guaranteed business outcomes by orchestrating 5G/6G networks, core functions, edge cloud, and hyperscale compute. Across verticals such as aviation, logistics, energy, public safety, retail, mining, and transport, Butlers deliver assured outcomes like real-time monitoring, passenger flow optimization, remote operations, and event-driven public safety. Each Butler dynamically combines connectivity, policy control, edge AI, and compute to meet SLA-backed performance targets, enabling enterprises to buy outcomes rather than bandwidth or static network products. The Catalyst addresses a critical industry gap: enterprises are rapidly adopting AI-native operations and demand deterministic outcomes with accountability, while CSPs remain constrained by legacy service creation, assurance, and monetization models. XAaS closes this gap through agentic intent translation, end-to-end orchestration, and outcome-based monetization aligned with TM Forum ODA. This allows CSPs to confidently design, quote, deliver, and monetize AI services using event- and outcome-driven billing. What makes the solution innovative is its shift of AI from an internal efficiency tool to a revenue-generating service layer. Customers pay only when outcomes are achieved, supported by a standardized Agentic Monetization Fabric spanning engagement, integration, commerce, and cloud runtime layers. The inclusion of a non-telco Enterprise Champion further grounds the solution in real buyer needs and validates commercial viability. Success is measured by the ability to shift CSP revenue from low-margin connectivity to high-margin AI services, accelerate time-to-market for new Butler offerings, improve enterprise intent accuracy and fulfillment speed, and increase enterprise adoption, retention, and SLA compliance. Ultimately, the Catalyst positions CSPs to capture the AI value layer and re-establish relevance as trusted providers of assured, AI-native services in the digital economy.

URN: C26.0.966

Project details

AI-native at scale: Elevating network optimization to CX

BACKGROUND In the fiercely competitive telecommunications industry, customer experience has become the core metric for Communications Service Providers (CSPs) to consolidate market share and achieve sustainable development. According to the GSMA 2025 Mobile Economy Report, global individual mobile user penetration will only register marginal growth over the next five years. This compels operators to shift their strategy from scale expansion to experience refinement, retaining users and driving growth by delivering superior service experience. CHALLENGE However, with the rapid rise of AI applications, ultra-high-definition video, live streaming, cloud gaming and other services, user expectations for network experience continue to escalate. Traditional network optimization only focuses on conventional network KPIs, and suffers from obvious limitations in user experience optimization — including difficult analysis workflows, long turnaround time and unsatisfactory improvement results. SOLUTION To address the core demands of telecom operators, this project launches the industry’s first vertical large model solution for network optimization built on the AI-Native Framework. Oriented toward tangible business outcomes and driven by digital twins together with domain-specific professional models, it enables a paradigm shift in operations and maintenance: transforming from humans executing and closing tasks with tools to humans supervising and augmenting AI Agent-driven task execution and closed-loop management, ultimately evolving toward the advanced stage of Agentic Operations. The solution improves key performance indicators including Net Promoter Score (NPS), user churn rate, top-tier OTT experience and complaint handling duration. It also deeply integrates into operators’ daily operational scenarios, builds a digital employee AI assistant matrix empowered by domain-specific large models, and delivers end-to-end intelligent upgrading of O&M operations. Built on China Telecom Qiming Large Model foundation, the solution leverages full-scale experience data collected by intelligent radio network elements, and incorporates a newly upgraded radio network optimization domain-specific large model. Combined with scenario-based capabilities of digital employees, it drives the transformation of network optimization logic from expertise-driven to data-and-AI-driven, shifting optimization priorities from pure network indicators to end-user experience. Timely optimization of dynamically changing wireless networks has long been a persistent industry challenge. As the core engine of the solution, the radio network optimization domain-specific large model relies on full-scale data collection and automatic document aggregation capabilities of intelligent radio network elements. It adopts two Transformer-based domain professional sub-models to form a closed-loop service experience optimization system:The User Experience Diagnosis Large Model (UELM) accurately identifies user experience anomalies through intelligent analysis of full-dimensional data from intelligent wireless networks.The Beam Space Large Model (BSLM) automatically completes problem diagnosis, simulation deduction and global optimization scheme generation. Optimized policies are directly delivered and enforced on live networks via digital employee assistants, achieving immediate network performance improvement and effective mitigation of potential complaint risks. This fully materializes the vision of shifting from users adapting to networks to networks proactively adapting to users. IMPACT Empowered by the disruptive design of the AI-Native Framework, the project delivers remarkable results across three dimensions to form a closed-loop value system: Outcome Delivery: Centering on core user demands, it achieves breakthrough improvement in key experience indicators. The average processing time of user complaint tickets is reduced by 37.6%, quality optimization indicators rise by 25%, and overall customer satisfaction is greatly enhanced. Domain-specific Digital Twin & Large Model Empowerment: Endowed with in-depth understanding of network mechanisms via the radio optimization vertical large model, the solution derives globally optimal overall schemes through simulation. It significantly reduces repetitive manual analysis and on-site testing, cutting on-site verification workload by over 50% in pilot areas, boosting radio optimization efficiency by more than 10 times, and substantially lowering operational costs. Operational Paradigm Upgrade: The entire user complaint handling process is greatly streamlined, enabling VIP complaint resolution within one hour. The automatic processing rate of poor-quality network optimization tickets increases by 11.2%, realizing the upgrade from passive problem response to proactive prediction and autonomous remediation. TM Forum Assets Used & Contributed Aligned with the TM Forum Open Digital Architecture (ODA), this project has deeply contributed to multiple TM Forum AIOps and DT4DI standards through practical implementation. It clarifies the core value and key application path of Domain Digital Twin (DTN) and domain large models within the AI-Native Framework, providing a replicable and scalable practical model for the AI-Native transformation of the global telecommunications industry.

URN: C26.0.979

Project details

LIA FieldOps: Autonomous AI agents for field technician support

Field operations are among the most costly and human-dependent domains in telecom. Technicians routinely rely on NOC and CO teams for troubleshooting guidance, authorization, and procedural support—creating bottlenecks that increase Mean Time to Repair (MTTR), limit workforce productivity, and drive up operational costs. LIA FieldOps directly addresses this challenge by providing real-time, natural-language operational support that safely transfers decision-making from humans to AI. LIA FieldOps introduces a new operational paradigm for communications service providers by placing autonomous AI agents at the center of field operations. Designed to act as the first operational decision layer for field technicians, reducing dependency on centralized Operations Centers, accelerates incident resolution, and enables a scalable path toward zero-touch field operations. Unlike traditional chatbots or rule-based assistants, LIA FieldOps operates as a context-aware, autonomous operational agent. It combines generative AI, structured operational playbooks, historical ticket intelligence, and live conversational context to diagnose issues, guide technicians through complex procedures, and autonomously resolve incidents when confidence thresholds are met. When uncertainty is detected, LIA performs an intelligent, structured handover to human engineers, preserving full diagnostic context, actions taken, and decision rationale—eliminating rework and reducing handling time. The solution is autonomy-by-design, continuously evaluating decision confidence using contextual signals, historical outcomes, and operational rules. A built-in learning loop ensures that every interaction strengthens the operational knowledge base, increasing autonomous resolution rates over time while maintaining strict safety, governance, and auditability. Architecturally, LIA FieldOps is a composable, API-driven platform aligned with TM Forum Open Digital Architecture (ODA) and integrated through TM Forum Open APIs with OSS/BSS, ticketing systems, and network management platforms. The solution has already been validated in a live Tier-1 CSP environment, demonstrating measurable reductions in human escalations even prior to full system integration. By reducing escalations, lowering MTTR, increasing technician productivity, and cutting operational costs, LIA FieldOps enables CSPs to decouple field productivity from centralized human availability. More importantly, it provides a controlled, measurable pathway toward Autonomous Networks Level 4, redefining how operational decisions are made and positioning AI agents as trusted, governed operational entities within the telecom ecosystem. LIA FieldOps does not simply automate field tasks—it transforms field operations into an autonomous, resilient, and scalable capability for the next era of telecom operations.

URN: C26.0.971

Project details

Boosting NPS beyond network KPIs with agentic AI

— TM Forum Catalyst · DTW June 2026 · C26.0.935 — Your network is performing. =========================== So why is NPS still falling? ============================ A Catalyst project tackling the blind spot at the heart of customer experience — and what happens when agentic AI is given the full picture. Agentic AI NPS Optimization Autonomous Networks IG1394 Customer Experience 5G — The Challenge — The gap between a good network and a happy customer --------------------------------------------------- Every CSP has invested heavily in measuring network quality. Customer Experience Indices built from hundreds of KPIs — coverage, throughput, latency, reliability — provide high-frequency, universal insight into how the network is performing. But boards don't track CEI. They track Net Promoter Score. And the link between the two is weaker than most operators expect. A customer sitting on a perfectly performing cell can still score you zero. Their activation took three days. Their billing dispute went unresolved for weeks. The self-care app crashed when they tried to pay. Customer care put them on hold for forty minutes. Network quality is necessary. It is not sufficient. The CEI → NPS Gap CEI High-frequency · Universal · Network-derived Coverage, throughput, reliability, latency → NPS Low-frequency · Survey-limited · True loyalty signal The KPI boards and execs live by Missing layer — the real NPS drivers Customer care quality Billing friction Onboarding experience Service provisioning Digital channel failures Incident handling Resolution time — The Transformation — From reactive surveys to real-time intelligence ----------------------------------------------- This Catalyst moves operators from a world of lagging indicators and siloed responses to continuous, proactive, cross-domain NPS management. Before Reactive NPS surveysFind out a customer is a detractor weeks after the fact, when it's already too late to act Network-only modelsCEI captures signal and speed — but misses care contacts, billing disputes, and onboarding failures entirely Siloed domainsNetwork, care, billing, and digital teams optimise independently — no unified view of customer experience Manual responseHuman-led diagnosis and intervention. Slow, inconsistent, and expensive at scale After Continuous NPS estimationNPS modelled in near-real-time from live network, care, billing, and digital signals — no survey lag Multi-signal intelligenceCEI augmented with every touchpoint that shapes customer perception — a complete picture of experience Cross-domain correlationNetwork, care, onboarding, and lifecycle data fused into a single NPS driver model per customer archetype Agentic closed-loop actionAI agents detect NPS risk, identify root cause, and trigger personalised interventions — autonomously — Use Cases — Three ways agentic AI improves NPS ---------------------------------- Each use case targets a distinct part of the customer journey where NPS is made — or lost. Together they form a closed loop from first impression to long-term loyalty. Use case 01 Intent-driven service onboarding The first hours of a new service define the relationship. Agentic AI monitors the activation journey in real time — detecting friction, adjusting parameters, and intervening proactively before a customer ever picks up the phone. Intent-based SLA management ensures the service delivered matches the service promised. 5G activation Intent management Onboarding NPS Use case 02 Proactive & personalised customer care Most care is reactive. This use case flips the model. By correlating network degradation, billing anomalies, and care contact patterns with NPS profiles by customer archetype, the system identifies at-risk customers and triggers personalised interventions — before they complain, and before they churn. Churn prevention Proactive care NPS archetypes Use case 03 Service lifecycle optimisation via coverage digital twins Network planning decisions have NPS consequences — but those consequences are rarely visible when they're made. This use case integrates NPS estimation into a coverage Digital Twin, so every planning decision — site deployment, sector configuration, upgrade prioritisation — shows its predicted customer impact. Digital twin Coverage planning Autonomous Networks — Solution Architecture — The full picture — from data to action -------------------------------------- The solution fuses four categories of data through an agentic AI layer aligned to TM Forum's ODA architecture, producing continuous closed-loop NPS actions across all three use cases. Data layer — inputs RAN KPIs (per-cell, hourly) Crowdsourced QoE Outage & incident data Customer care contacts Billing & payment events Onboarding & provisioning Digital channel telemetry NPS survey samples ↓ ↓ ↓ Agentic AI layer — correlation, learning & decision NPS estimation models KPI driver extraction Customer archetype profiling Knowledge graph (IG1394) Intent translation agents Care recommendation agents Lifecycle optimisation agents ↓ ↓ ↓ Actions layer — closed-loop outputs Onboarding intervention Proactive care trigger Retention offer Network parameter adjustment Coverage planning recommendation NPS lifecycle reporting ↕ ↕ ↕ TM Forum standards alignment IG1394 NPS framework ODA architecture Autonomous Networks levels TMF Open APIs IG1253 Intent Mgmt IG1463 Agentic AI security — Expected Impact — Measurable outcomes, not just insights -------------------------------------- Success is defined by what moves — across NPS accuracy, customer loyalty, care efficiency, and operational autonomy. +5–10% NPS uplift across targeted use cases and customer segments −1–2% Churn reduction driven by proactive AI-led care interventions +20–30% Customer care efficiency improvement through intelligent automation 90% Faster diagnostics in care scenarios enabled by multi-signal AI — Standards Alignment — Built on TM Forum best practices -------------------------------- Every architectural decision is grounded in TM Forum frameworks, ensuring the approach is portable, interoperable, and ready for production adoption by any CSP. Asset Role in this Catalyst Applied to IG1394 NPS correlation & management framework — Product, Service & Network NPS layers All use casesCore model Autonomous Networks levels Automation maturity framework — measuring progression from assisted to closed-loop operation Use case 02Use case 03 ODA Open Digital Architecture — component model guiding the solution's modular design Architecture TMF Open APIs TMF629, TMF632, TMF641, TMF628, TMF642, TMF683 and others for customer, service & network integration Integration layer IG1253 / IG1358 Intent management standards underpinning the onboarding use case and SLA/SLO translation Use case 01 IG1463 Security in agentic AI for Autonomous Networks — governing agent behaviour and trust boundaries AI layer The goal: Transforming NPS from a survey metric into a real-time operational KPI — one that CSPs can measure, predict, and act on every day.

URN: C26.0.935

Project details

Agent fabric: A2A-T runtime - Phase III

C26.0.910 · Agent Fabric: A2A-T Runtime – Phase III Agents that understand telecom. Trusted autonomy for production. Specialized AI agents across telecom's Business, Service, and Network layers today lack a common task language. Multi-agent interactions are unstructured, error-prone, and chat-driven. Closed-loop automation fails in production because agents cannot negotiate, delegate, or report outcomes in a standardized way. This Catalyst delivers a production-grade, trusted,secure runtime implementing A2A-T — a telecom extension of the A2A protocol — that moves from chat-based AI to protocol-driven autonomous agency. Three high-value scenarios are demonstrated end-to-end: HVS 1 — Service Fault Management (Problem Resolution). Cross-domain autonomous fault resolution spanning RAN and Transport network domains. A four-agent chain — Anomaly Detection, Problem Identification, Diagnostics, Impact Analysis, and Prioritise & Recommend — executes without human intervention, with subscriber impact surfaced through a Customer Service Digital Twin. HVS 2 — Enterprise Service Complaint Handling. An enterprise SLA breach triggers a fully autonomous complaint resolution flow across business and service layers, returning a structured outcome with audit trail and SLA credit determination. HVS 3 — Enterprise Service Fulfilment. An enterprise order triggers end-to-end autonomous service provisioning across access, transport, and service layers — coordinating across internal and external agents to compress fulfilment timelines and eliminate manual handoffs. Cross-cutting — Federated Agent Capability Discovery. A fabric-level capability demonstrating semantic, multi-ontology agent discovery — closing the gap between syntactic A2A-style registries and LLM-assisted capability matching across industry boundaries. Runs across all three HVS. The A2a-T runtime comprises three components: an Agent Registry Center with structured skills ontology and knowledge graph; an A2A-T Protocol Runtime implementing Task-T, Event-T, and Negotiation-T over a live Python/FastAPI and React/WebSocket stack; and a Trust and Audit layer covering agent onboarding and operational observability. The Catalyst targets DTW 2026 with a live, multi-vendor demonstration using real agent software and real operator data. Protocol code and reference implementations developed through this Catalyst will be contributed as open source to the Linux Foundation, making A2A-T available to the broader telecom and agentic AI community beyond TM Forum.

+20

URN: C26.0.910

Project details

Composable, headless & AI-agentic CRM

Breaking the CRM Monolith to Power the TechCo Future The Composable, Headless & AI‑Agentic CRM Catalyst demonstrates how communications service providers can replace rigid, monolithic CRM platforms with a flexible, AI‑native architecture designed for the TechCo era. Built in alignment with TM Forum Open Digital Architecture and Open APIs, the project shows how CRM can evolve from a static system of record into a dynamic system of action—ready for AI‑driven engagement, ecosystem scale, and rapid innovation. Legacy CRM platforms impose a “Monolith Tax”: high licensing costs, slow customization cycles, brittle integrations, and limited data liquidity. This Catalyst tackles those constraints head‑on by introducing a headless, composable CRM where customer data, identity, and intelligence remain under CSP control and are accessible to both human users and AI agents. Decoupling data and logic from presentation enables any digital channel, partner, or vertical solution to plug in without re‑engineering the core. AI agents are treated as first‑class CRM users, capable of interpreting customer intent, invoking TM Forum Open APIs, and autonomously fulfilling, assuring, and managing services with built‑in guardrails and auditability. This approach dramatically reduces time‑to‑market for new offers—from months to days—while enabling zero‑touch fulfillment at scale and lowering total cost of ownership. By enabling modular deployment on an ODA Canvas runtime and standardizing interaction through TMF Open APIs, the Catalyst provides a repeatable blueprint for AI‑driven CRM transformation. The result is a platform that supports ecosystem monetization, AI‑powered marketplaces, and vertical‑specific digital services—allowing CSPs to move beyond connectivity and become true digital platform orchestrators in an AI‑first economy

URN: C26.0.994

Project details

The big deal - Phase II

The "Big Deal" tackles one of the most persistent challenges in B2B telecom: the complexity of quoting, configuring, and ordering enterprise services at scale. Despite ongoing digital transformation efforts, many Communication Service Providers (CSPs) still rely on manual quoting processes for complex enterprise deals. This results in slow response times, lost opportunities, unvalidated configurations, costly manual rework, and revenue leakage when orders must be adjusted post‑sale. At the same time, rigid product definitions and inflexible catalogs make automation difficult, turning transformation initiatives into long, expensive programs. What This Catalyst Delivers This Catalyst demonstrates a new, end‑to‑end approach to enterprise product commercialization—moving from manual, error‑prone processes to fully automated, intent‑driven quoting and ordering. At the heart of the solution is a runtime, TM Forum–compliant catalog capable of modeling even the most complex enterprise products and their relationships. By introducing new catalog modeling patterns and AI‑assisted product ingestion, CSPs can rapidly productize offers, automate quoting and fulfillment, and dramatically reduce time‑to‑market and time‑to‑transform. Key Innovations Intent‑driven digital and assisted channels Customers interact through intuitive, intent‑based self‑service channels, while sales teams use advanced solutioning tools for assisted selling—removing complexity from the customer experience. AI‑accelerated product modeling AI ingests existing product specifications and documentation to automatically generate catalog models, enabling rapid onboarding of products and faster transformation of legacy portfolios. Advanced catalog composability and relationships New modeling patterns manage complex dependencies across products and services, providing guardrails for fulfillment and enabling goal‑seeking automation and AI‑driven orchestration. Supplier and access option intelligence A supplier scanner matches customer intent to the best third‑party access options by geography, enabling optimal commercial and technical decisions. One product, many markets A single catalog model supports multiple markets with different currencies, pricing, regulations, languages, and feature availability—dramatically improving global scalability.

URN: C26.0.967

Project details

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