A Securities Firm's AI Transformation: Building an Intelligent Knowledge Hub with KAIHE Cloud Gateway
Hua'an Securities (pseudonym) manages approximately 80 billion RMB. In 2026, its digital transformation office faced a classic dilemma: hundreds of daily research reports, mandatory compliance reviews, and customer service policy retrieval -- three workflows, three systems, severe information silos. Regulatory requirements strictly prohibited uploading any data to external AI platforms.
The Solution: Two Weeks to Deployment
In March 2026, the team deployed KAIHE AI's cloud model aggregation gateway:
Week One: Deployed KAIHE gateway on internal servers; connected DeepSeek-V3 (reasoning), Qwen-Long (long documents), and a local private model (compliance); integrated 3,000+ research reports, compliance database, and customer FAQ.
Week Two: Built three scenario agents -- Research Assistant (auto-summarization, cross-reference, real-time industry comparison, 30-second structured briefings), Compliance Review (auto-scanning for sensitive content, citation accuracy, risk disclosure -- all within internal network), and Customer Knowledge (structuring scattered PDFs into searchable knowledge graphs).
Results: Transformative Efficiency
| Metric | Before | After | Change |
|---|---|---|---|
| Report review | 4.5 hrs/report | 2.7 hrs | -40% |
| Knowledge retrieval | 8.2 min/query | 3.1 min | -62% |
| Briefing generation | 3 hrs manual | 30s + 5min review | -97% |
| Compliance coverage | 30% sampling | 100% | +70pp |
Additional benefits: accumulated knowledge graph, new employee onboarding shortened from 3 weeks to 5 days, compliance risk reduced through full coverage.
Why KAIHE?
Data stays on-premises (non-negotiable), flexible multi-model switching under one gateway (not three vendor APIs), scalable architecture for progressive expansion.
Industry Implications
Mid-sized financial institutions are choosing pragmatic AI deployment paths. They don't need to build thousand-billion-parameter clusters -- they need a model aggregation layer: connect the best models, keep data on-premises, match tasks to optimal models, and deliver value within weeks. That's KAIHE's positioning. The 2026 dividing line isn't which "strongest model" you use, but whether you've built an orchestration system ensuring the right model does the right job.