AI applications, business analysis, content tools, and user validation. Different contexts, one approach: define the problem, move implementation forward, and deliver evidence.
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.
Turn trending news into content ready for production
Organize news retrieval, source verification, topic selection, and spoken-script generation, delivering titles, hooks, subtitles, and direction for AI-generated visuals.
Recombine and reuse existing assets in batches
Build on an existing open-source Skill to organize asset grouping, intermediate outputs, script checks, and finished-video archiving into an operational workflow.
Meet users and put a product idea to the test
Explore a new product for families with autistic children, combining store observations and user feedback to develop a physical demo of “Hug Bread.”
Case outcomes are drawn from my personal materials; team achievements, prototype validation, and real business use are identified separately.
Career opportunities, project collaboration, or a conversation about bringing AI into real work.