Reported impact
Business outcome
After a three-year AI transformation, AsiaInfo brought nearly ten thousand R&D staff into an AI-coding operating model, lifted code output per engineer about 14.6%, improved average end-to-end efficiency from requirements through testing 8.6%, reached about 80% AI coverage across core BSS systems, and maintained more than 90% monthly activity in a 1,000-person operations team while turning senior experts' tacit know-how into reusable team capability.
- Productivity Improvement
- Speed to Market
Solution
AsiaInfo moved its engineering organization toward an AI-native delivery model by deploying Alibaba Cloud Qoder across requirements, design, coding, testing, and defect repair for complex telecom BSS and OSS systems. Qoder's knowledge engine ingests historical code, API assets, architecture characteristics, and development conversations, extracting norms and experience into searchable knowledge cards so expertise no longer stays trapped with senior specialists. Engineers and AI agents retrieve those cards during work, while human architects use Spec to define module boundaries, interface contracts, and agent role constraints. In complex defect scenarios, Qoder splits work across agents for fault localization, repair, and verification that share context and recompose results at the architecture layer. The platform acts as both an enterprise knowledge layer and a multi-agent coding workflow, turning decades of expert know-how into reusable team capability across a large R&D organization.
Data flow
Historical code, API assets, architecture features, and ongoing development conversations are ingested into Qoder's knowledge engine, which turns them into searchable knowledge cards. Engineers and AI agents retrieve those cards during requirements, design, coding, testing, and defect repair; for complex defects, architects define module and role constraints with Spec, work is split across localization, repair, and verification agents, and the results are recomposed into the BSS/OSS development flow.