Before the key enrollment period, the institution launched a generative search visibility optimization project. Due to scattered information sources and inconsistent answer structures across mainstream AI platforms on enrollment policies, program settings, and fee structures, potential user decision efficiency was affected.

FutuneAI based on user high-frequency decision paths, systematically organized and restructured core application questions, established a unified information expression framework around key policy information, project value and differentiation points, and completed multi-platform adaptation and verification.

Within approximately 3 weeks, major high-frequency questions formed stable, reusable answer structures across multiple mainstream generative AI platforms, with significant improvement in key information coverage and model citation consistency and accuracy, providing a reliable foundation for generative search reach during the enrollment phase.

The platform aims to improve brand visibility and priority positioning in high-frequency questions such as "tool recommendation", "usage comparison", and "query entry" in generative search and AI Q&A scenarios.

FutuneAI used real user questions as the core basis to systematically optimize the brand's role positioning, mention probability, and recommendation context in mainstream generative AI platform answers, and through structured content adjustments, improved model understanding consistency of brand value and use cases.

After project launch, within 10 days, the brand achieved the target mention performance in core questions, with multiple platforms gradually forming stable recommendation structures and establishing a clear, recognizable brand position in generative search scenarios.