A low-code platform for building, deploying, and adapting AI-powered vision pipelines on edge hardware. Built for agriculture, critical infrastructure, and defence.
Get in touch →To scale a CV system beyond a demo, teams typically need 5–8 specialist engineers and over $500k/year in infrastructure. Most projects stall, many companies run out of funding before they reach product. nLens replaces that stack.
nLens is built around two interoperable layers that remove the infrastructure burden from computer vision teams.
Multi-camera ingestion with sensor diagnostics, PTZ control, and adaptive sampling. Handles occlusion, noise, and degraded feeds across constrained and congested environments without manual triage.
A visual signal intelligence layer combining object detection, multi-target tracking, and vision-language reasoning. Build event-detection and classification logic without writing infrastructure code.
One-click deployment to constrained hardware. Feedback-driven auto-tuning that adapts to shifting real-world conditions. No MLOps team required.
Drag-and-drop pipeline composition using pre-built logic blocks: trackers, detectors, geo-filters, anomaly classifiers, and conditional rules. Engineers automate decisions without specialist infrastructure knowledge.
nLens is validated in environments where cloud-native pipelines fail: unstructured, sensor-rich, and operationally critical.
We are looking for pilot partners, sector collaborators, and early commercial relationships in agriculture, infrastructure, and defence.