Stock Markets June 3, 2026 10:02 AM

CLPS Unveils AI Rainstorm Factory, Eyes Faster R&D as Shares Rise

Company rolls out modular AI development framework and expands server clusters across Asia to accelerate delivery cycles

By Sofia Navarro CLPS

CLPS Inc reported a strategic overhaul of its research and development processes centered on an AI-driven model it calls the AI Rainstorm Factory. Shares rose 5.4% after the company outlined a specification-driven, six-phase framework and disclosed expanded computing infrastructure in Shanghai, Shenzhen, and Singapore with peak capacity up to 15,000 teraflops. Management framed the move as building foundational AI capabilities to improve delivery speed and lower operating costs while preserving project quality.

CLPS Unveils AI Rainstorm Factory, Eyes Faster R&D as Shares Rise
CLPS

Key Points

  • CLPS launched the AI Rainstorm Factory, a six-workshop AI-driven model intended to automate R&D and development workflows.
  • The company has deployed server clusters in Shanghai, Shenzhen, and Singapore, reporting peak computing capacity of up to 15,000 teraflops and an internal AI service platform based on open-source models.
  • Management said the restructuring aims to cut project R&D and delivery timelines by up to 50% while preserving delivery quality and lowering operating costs - impacts are most relevant to software development and IT infrastructure sectors.

CLPS Inc saw its stock climb 5.4% on Wednesday after the company announced a major restructuring of its research and development function that places artificial intelligence at the center of software development and delivery.

The company introduced an initiative it has named the AI Rainstorm Factory. The program is presented as a reimagining of the traditional software development lifecycle into a streamlined, specification-driven framework that automates core stages of R&D. CLPS said the approach is intended to reduce the time required for project R&D and delivery cycles by up to 50% compared with conventional workflows.

To support the model, CLPS has deployed AI infrastructure and server clusters across its offices in Shanghai, Shenzhen, and Singapore. The company reported establishing computing capacity with peak performance of up to 15,000 teraflops. In parallel, CLPS has built an internal AI service platform that is based on open-source models.

The architecture of the AI Rainstorm Factory is divided into six specialized workshops, each aligned to a segment of the development process. The workshops focus on UI design, project management, business requirements, technical architecture, agile R&D, and automated testing. CLPS said each workshop applies AI technology to improve efficiency in its respective phase.

CLPS described the restructured approach as a specification-driven collaborative framework that spans six phases - from project initiation and requirements analysis through architectural design, code development, and final testing. The firm emphasized that automation and AI augmentation are intended to compress manual steps across these stages.

Alongside the development framework, CLPS is creating an AI for IT Operations resource management system. The tool is designed to monitor and allocate computing resources, produce analytics to guide future hardware procurement decisions, and assist in training enterprise-grade large language models.

Chief Executive Officer Raymond Lin characterized the strategy as an effort to build foundational AI capabilities internally rather than solely consuming third-party AI tools. He said the restructuring is a strategic move to reshape the company ompetitive position while aiming to maintain project delivery quality and reduce operational costs.

CLPS also indicated plans to continue expanding its AI server infrastructure and to scale computing power to support the AI Rainstorm Factory across its global operations.


Contextual note - The company framed the initiative as both a technical and operational reconfiguration of its R&D organization, with a focus on automation, internal platformization, and capacity planning based on usage analytics.

Risks

  • Execution risk in implementing the AI Rainstorm Factory across projects and locations - affects software development timelines and delivery certainty.
  • Capital and procurement uncertainty tied to scaling server infrastructure - impacts IT infrastructure and data center resource planning while driving hardware purchase decisions.
  • Operational trade-offs as the company seeks to reduce costs and compress delivery cycles without compromising project quality - this introduces performance and quality-control uncertainties.

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