Connecting Academic Insight with Deployable AI.
We help research teams and organizations turn complex evidence, domain knowledge and AI engineering into work that can be reviewed, delivered and maintained.
Two routes. One standard.
The homepage routes visitors quickly. Each specialist page owns its detailed scope, deliverables and boundaries.
Typical scenarios / Academic Research
Choose a scenario track here, then select a working situation below. The detail, expected output and image change together.
Map journals by CAS quartile, JCR category, discipline scope and indexing system before narrowing a research route.
Service boundaries
Responsible research and maintainable systems begin with a shared understanding of what the work includes and what remains with the client.
How we work
The same four-stage structure governs both research support and AI delivery, while the detailed checkpoints remain specific to each track.
Review context, users, current materials, objectives and constraints before defining a promise.
Standards and safeguards
These principles define how materials, decisions and ownership are handled throughout the work.
No fabricated evidence, guaranteed publication, substitute authorship or hidden third-party work.
Every academic support task keeps the author and research team responsible for judgement, evidence and final decisions.
About Lianyin
Hebei Lianyin Technology brings academic judgement and engineering delivery into one working system. The company is organized around two specialist teams with a shared standard for rigor, evidence and maintainability.
Start with context
Share the objective, current materials, expected result and timing. We will route the conversation to the relevant specialist track.