Hikvision's Guanlan Large-Model System at WAIC: "More, Faster, Better, Cheaper" to Move AI From Understanding to Doing

Making AI understand the physical world like a human

As AI leaves the screen for the physical world — living in robots, cameras and inspection devices — the key to deployment is whether these carriers can understand the physical world as humans do. At WAIC 2026 on July 17, Hikvision released the Guanlan Large-Model Whitepaper (2026) and showcased the full Guanlan system. As a "physical-world" model, it processes video, images, language, audio, X-ray and mmWave IoT sensing, with vertical adaptation per industry.

The system: more, faster, better, cheaper

Guanlan spans "foundation model — vertical model — model products — industry applications," with four systemic strengths:

• More: a full IoT-sensing matrix of vision, X-ray, mmWave and language; over a thousand cloud-edge-device products; 90+ industries, 2,000+ scenarios, 600+ solutions.
• Faster: few-shot registration, zero-shot startup; a complete deployment toolchain, hardware-software out of the box.
• Better: processing raw signals directly to preserve information — perimeter false alarms down 90%+, industrial micro-hole defect detection at 99.99%, X-ray security screening detection above 97%.
• Cheaper: edge-cloud collaborative architecture with flexible big/small models and inference optimization to cut compute and bandwidth cost.

From "chatting" to "working"

If earlier AI focused on chatting, today's AI focuses on doing — and shifting from passive to proactive: multimodal cameras switch modes by voice and proactively flag anomalies; industrial models raise yield on the factory floor. Hikvision's path represents one paradigm for physical-world AI — not conversational showmanship, but embedding models into real workflows across industries via "more, faster, better, cheaper."