I built a maritime surveillance AI for an upcoming HP ZGX Nano demo with the U.S. Navy. It processes reconnaissance imagery in under 5 seconds—fully local. No cloud, no network, no data leaves the device.
Federal agencies face a real gap: commercial laptops can’t run strong vision-language models locally, but classified imagery can’t go to cloud APIs. That leaves analysts with either no AI or unusable AI. This demo closes that gap.
Upload an aerial image to the demo and the system identifies vessel type, assesses cargo and activity, then produces a structured threat assessment with actionable recommendations.
It uses Salesforce’s open-source BLIP-2 VLM for visual understanding and TinyLlama to generate natural-language intelligence reports. Everything runs on an HP ZGX Nano—smaller than a desktop PC, with 128GB unified memory—deployable on ships, mobile command centers, or in disconnected SCIFs.
Models are open source. No classified data—only public imagery and synthetic coordinates. Code is on GitHub: https://github.com/curtisburkhalter/Fed-Navy-demo
For public-sector procurement and IT teams, this is what local AI looks like in practice.
NOTE: This demo was built independently as an HP product demo with publicly available data. The U.S. Navy had no involvement, review, or endorsement to it.
See the full post on LinkedIn.