High-quality data development
Explore data creation, annotation guidelines, and quality assessment around specialist knowledge and task design.
Academic Collaborations
Connecting academic expertise with real industry needs
Collaborate on talent development, data creation, and joint research to connect academic expertise with industry projects.
- University collaborations
- 100+
- Specialists in our talent pool
- 1000+
Deep collaboration with 100+ universities
Our network includes Peking University, Tsinghua University, and many more. We connect multidisciplinary expertise and a talent pool of 1000+ specialists with education, joint research, and industry practice.
Representative university collaborations
- Peking University
- Tsinghua University
- Harbin Institute of Technology
- Northeastern University
- Dalian University of Technology
- Huazhong University of Science and Technology
- University of Electronic Science and Technology of China
- And more — 100+ universities
From talent development to joint research
Explore ways to connect teaching, practical experience, specialist development, and research around different collaboration needs.
Course collaboration
Connect AI and data engineering challenges with teaching. Opportunities include specialist courses, project assignments, and exchanges on industry practice.
Internship and practical training bases
Introduce real business contexts through data creation, model evaluation, and engineering practice. Staged training, project work, and reviews can help students understand teamwork and quality requirements.
Targeted talent development
Explore staged learning and talent matching around project needs and role requirements. Jointly design development paths covering subject foundations, task practice, and skills assessment.
Joint laboratories
Explore shared research topics, technical validation, and research translation. Start with a concrete question and a small-scale study to define responsibilities and milestones.
Joint research and innovation
Connect academic strengths with industry needs to support interdisciplinary work and technical applications. Explore joint research around data, algorithms, and practical scenarios.
Joint research areas
The following are potential areas for collaboration, shaped around specific needs.
Foundation model evaluation
Explore evaluation tasks, scoring criteria, difficult examples, and result review around model capabilities and real-world use.
Domain knowledge engineering
Explore knowledge organization, content validation, and domain applications using disciplinary expertise and specialist judgment.
Research engineering
Connect research prototypes with engineering practice through method reproduction, tool development, and scenario validation.
Start a collaboration with a real question
We welcome university faculty, research teams, and industry partners to discuss courses, talent development, joint research, and technical validation. Together, we can define goals, participation, and practical milestones.