Turn unclear requirements into an actionable plan
Map business processes, set priorities, and define MVP scope and acceptance criteria.
AI-HR · SOP mapping across 8 recruitment stagesI'm Zhaoyu / Joel. I connect business problems, product thinking, and AI practice. From frontline product support to building agents and workflows, I care about whether technology can become part of real work and solve concrete problems.
Map business processes, set priorities, and define MVP scope and acceptance criteria.
AI-HR · SOP mapping across 8 recruitment stagesConnect models, knowledge bases, and tools to build agents, Skills, and application prototypes.
ValueGuard · A three-stage agent decision workflowResolve product issues, support training, and document processes and methods in manuals and SOPs.
ClassIn · 26 on-site support engagements and 2 product training sessionsEach case explains the problem I faced, the decisions I made, and what has been validated so far.
Evidence-based after-sales decisions, with human oversight
Turn customer signals from multiple sources into priority assessments, explanations of causes, and recommended actions to help triage after-sales exceptions involving high-value customers.
Understand recruitment first, then choose where AI fits
Organize fragmented recruitment activities into standard tasks, clarifying the handoffs between AI processing, human review, and exception handling.
From repetitive answers to a support process others can take over
Starting with frequent business inquiries in QQ groups, design a workflow for knowledge lookup, context completion, and escalation of complex issues to a human.
In frontline support at ClassIn, I resolved real product issues, helped customers use the product, and turned that experience into materials the team could reuse.
Explore my experienceOn-site product support at universities
Product training sessions led independently
Training attendances
On-site support → Manuals and tutorials
Training experience → Standardized SOPs
My big-picture, framework-level thinking about AI products, presented as a mind map.
From local efficiency to business outcomes: define the goal, identify constraints, and use an MVP to test the most critical uncertainty.
Distinguish creating value from capturing it: once AI is no longer the bottleneck, look again at the variables limiting opportunities, resources, and outcomes.
Career opportunities, project collaboration, or a conversation about bringing AI into real work.