An AI-native, edge-first video intelligence platform that turns any CCTV or IP camera into a cognitively capable sensor — searchable in plain language, alerting in real time, and fully functional even with zero connectivity.
Video surveillance infrastructure has scaled far faster than the human capacity to watch it — a single operator can meaningfully attend to only 8–12 feeds at once, while the rest goes unreviewed until it's too late.
Only a small fraction of captured footage is ever watched — incidents surface too late to act on.
Blind spots grow linearly with every camera added — human monitoring hits a hard ceiling.
Legacy platforms treat AI as a plugin — high false-positive rates lead to alert fatigue and disabled systems.
NeoVision AI sits above any camera, any recorder, and any cloud — converting raw footage into structured, queryable understanding in real time, with detection running at the edge first and the cloud as an optional layer for aggregation and cross-site search.
Every frame that matters becomes structured metadata — objects, attributes, actions, events.
→Metadata is indexed for retrieval by keyword, filter, vector similarity, or plain language.
→Matching events trigger sub-second multi-channel alerts, with humans kept in the loop for action.
Each pillar is an independently deployable capability, unified by the same underlying event model and search index.
Find any moment across hours of footage in plain language.
Sub-second detection with automated multi-channel alerting.
Climbing, crawling, fence-cutting, digging, and tampering detection.
Accident detection, plate recognition, adaptive signal control.
Rapid post-incident search across existing recorder archives.
Incident localisation using regional satellite positioning.
Full intelligence embedded in-camera, no network dependency.
The core modules each have a dedicated page covering their functional requirements, performance targets, and business value in detail.
A layered architecture, from embedded silicon through to command-centre presentation, so time-critical detection never depends on a network connection.
Cross-camera search to locate a person or vehicle in seconds instead of hours.
City-wide safety and traffic monitoring without the alert fatigue of legacy analytics.
Fire and unauthorised-zone detection where point sensors miss open-area hazards.
Every request — human or service-to-service — is authenticated, authorised, and logged. Built to satisfy data-sovereignty and air-gapped requirements from day one.
Full stack inside your own data centre, no external dependency.
Deployed within a customer-controlled, dedicated cloud tenancy.
Edge inference on-site with aggregation and search in the cloud.
Zero outbound connectivity, with updates delivered via physical media.
Video ingestion, object and fire/smoke detection, live dashboard, single-site deployment.
Natural-language search, geospatial incident mapping, traffic module, multi-tenant command centre.
Air-gapped packaging, hardware root-of-trust, specialised perimeter and tamper-detection models.
Chip-level inference on embedded hardware, OTA update pipeline, power-optimised for outdoor use.
In-camera inference at scale, a conversational investigation assistant, and predictive analytics.
Partner with Neveon Technologies to design, pilot, and scale a video analytics platform built for your environment.