RackVision AI

### Problem Global enterprise data centers currently operate with a critical "Visibility Gap." Traditional network management systems monitor logical traffic and software logs but remain completely blind to the physical space. When an onsite technician over-bends a fiber line or miswires a high-density GPU rack, the logical system drops packets, and engineering teams waste hours guessing why. ### Solution RackVision AI bridges this gap as a Physical-to-Logical Digital Twin Platform. We turn real-world visual telemetry into intelligent infrastructure workflows, giving operators full visibility across both the physical data center floor and the network layer. ### How We Built It (Technology Stack) * **Google Gemini Flash (via Gemini API & Google AI Studio):** Powers our Vision-Language pipelines. It acts as our autonomous workspace inspector, processing low-latency live video streams to evaluate hardware device health, track port alignment, and identify physical cable strain. * **Google Gemini Pro:** Drives our "Sketch-to-Topology" simulation engine. It interprets geometric, hand-drawn layout photos from field engineers and instantly converts them into interactive digital twin maps. * **Veea Lobster Trap:** Our mission-critical AI governance layer. Because an infrastructure agent handles production switch logic, we deployed Lobster Trap as a Deep Prompt Inspection proxy firewall. It blocks malicious prompt injections, intercepts unauthorized credential extraction commands, and logs an unalterable, regulator-ready compliance audit trail. ### Track Alignment: Track 3 (Robotics & Simulation) RackVision AI directly addresses Track 3 focus areas by deploying multi-modal Vision-Language models to interpret real-world spatial environments and compiling hand-drawn drafts into scalable, digital twins for industrial IT environments.