AI Full-Stack R&D Intelligent Framework - IRON-M (Incisive Regenerative Overall Natural) Qorus-NTT DATA Innovation in Insurance Awards 2026

(Ⅰ) Concept and Objectives Focusing on the core competitive factors of enterprises—R&D efficiency and talent capability enhancement—this project IRONs to build a full-stack AI-driven R&D framework that deeply integrates artificial intelligence capabilities with R&D processes. Spanning the entire R&D lifecycle from demand generation and analysis, architectural design, coding, and testing & validation to deployment & operation, the framework embeds AI agents to create a smarter, more automated, and collaborative R&D workflow. The core objective is to enable every developer to acquire full-stack capabilities like "Iron Man" through the project's innovations, allowing them to independently complete end-to-end development tasks without relying on multiple roles or team collaboration. This enables junior engineers to reach the professional level of senior experts while significantly improving the efficiency of senior developers, driving the evolution of R&D models from traditional toolchain upgrades to fundamental transformations. (Ⅱ) Underlying Reasons Traditional development models face numerous prominent pain points: business teams manually write requirements documents, product managers design specifications and UIs, systems analysts decompose requirements, developers are responsible for coding, and testers complete test case writing and manual test data generation. Each link requires substantial human input, and even with agile iterations, issues such as misunderstandings between phases, coding standard violations, software defects, configuration vulnerabilities, and data errors frequently occur, leading to low efficiency, slow delivery, and delayed responses. Currently, breakthroughs in large language models and improvements in computing power have elevated AI from an "auxiliary tool" to a "core productivity engine". The maturity of technologies such as multimodal understanding and intent recognition has made it possible to address industry pain points. Meanwhile, industry competition focuses on the comprehensive integration capability of "data—computing power—use cases", and enterprises urgently need to build AI-native R&D systems to create technological moats, achieve cost reduction, efficiency improvement, and digital transformation. (Ⅲ) Competitive Landscape Most R&D tools on the market are single-function AI-assisted tools or traditional R&D toolchains, which can only cover partial links in the R&D process and lack end-to-end connectivity. In contrast, the IRON Framework achieves full lifecycle coverage, integrating 21 dedicated AI agents and intelligent support for 37 key activity nodes in the R&D chain, including core functions such as automatic demand generation, intelligent architecture diagram drawing, code snippet recommendation, test case generation, and automatic defect repair. Compared with similar products, its unique multimodal technology supports diverse interactions such as text, charts, code, and voice; the dynamic upgrade cycle enables system self-evolution; and the zero-threshold access design reduces usage costs. It effectively solves common industry problems such as inconsistent information and misunderstandings in collaborative programming, forming significant competitive advantages in efficiency improvement, quality assurance, ease of use, and scalability. (Ⅳ) Source of Inspiration The inspiration comes from dual drivers: on the one hand, opportunities brought by technological evolution— the maturity of AI technology makes deep integration into R&D processes possible, providing technical support for reshaping R&D models; on the other hand, the core needs of enterprise development—faced with digital transformation, organizational capability enhancement, and fierce market competition, traditional R&D models can no longer meet the requirements of efficient and high-quality delivery. There is an urgent need to release individual and organizational potential through innovative means. Therefore, the idea of empowering the entire R&D process with AI, building an intelligent framework with self-evolution capabilities, redefining the role of developers and R&D models, and realizing the role transformation from "human-machine" to "machine-human" emerged. (Ⅴ) Involved Departments The project core involves departments related to the entire R&D chain, including requirements analysis teams (responsible for demand mining and decomposition), architectural design teams (participating in intelligent architecture planning), coding and development teams (covering front-end and back-end development), testing and validation teams (leading test case design and defect detection), and deployment and operation teams (responsible for system launch and operation support). At the same time, business departments are required to cooperate in demand communication and confirmation, forming a cross-functional collaborative promotion model. (Ⅵ) Key Achievements to Date Since its launch in 2025, remarkable results have been achieved:In terms of core operational data: demand throughput increased by 39%, delivery cycle shortened by 20%, production defects reduced by 42%, demand defect rate decreased by 46.97%, and defect re-opening rate reduced by 50%;In terms of technology application: AI penetration in the R&D lifecycle reached 74%, AI-generated code accounted for 38% (an increase of 32 percentage points from the beginning of the year), equivalent to saving 171 full-time personnel, with AI-generated code exceeding 4 million lines;In terms of functional implementation results: 104 tool upgrades were completed throughout the year, establishing a sound AI coding application mechanism; 14,000 AI-assisted demand decompositions were completed, with an average agent penetration rate of 52%; AI-generated test cases identified 4,495 defects; intelligent operation and maintenance automatically handled 61,000 incidents, with a penetration rate of 40.9%, and the efficiency of single incident handling increased by up to 60 times.