Passive recording
Many camera systems capture footage but provide limited operational intelligence.
Turn compatible camera deployments into a managed local video and intelligence layer — processing selected workloads closer to the cameras and synchronizing only what the use case requires.
Recording, storage, analytics and remote access may sit across separate systems. Cloud-heavy designs can also move more video off-site than the customer actually needs.
Many camera systems capture footage but provide limited operational intelligence.
NVR, analytics and management can become separate boxes and support paths.
Sending every stream continuously off-site can increase bandwidth and centralized processing requirements.
The technology stays behind the customer outcome.
Use the ARED edge platform for compatible local storage, selected analytics and remote platform operations instead of adding another independent compute layer.
Recording and selected analytics can run near the cameras, subject to deployment design, power, storage and application requirements.
Start with the required camera workflow, then add compatible analytics or integrations as capacity and business value are proven.
ARED is hybrid by design. Local-first does not mean cloud-free.
Existing or planned camera streams are assessed for protocol, codec, resolution, bitrate and interface compatibility.
Can ingest compatible streams, provide local storage and run selected edge analytics where the gateway profile supports them.
Send alerts, metadata, selected clips, reporting or backups when the deployment requires them.
Camera model, stream compatibility, storage retention and AI capacity must be assessed before production deployment.
Store compatible camera footage locally according to validated storage and retention requirements.
Connect compatible IP camera streams without assuming every camera model or codec is supported.
Run approved local analytics where gateway capacity and model compatibility have been verified.
Send selected events, metadata or alerts instead of moving every raw frame continuously.
Monitor the managed edge platform and supported camera-service health remotely.
Synchronize selected clips, events, backups or reporting data when required.
Use existing compatible cameras for local storage and selected operational intelligence.
Add a managed edge layer to compatible security and smart-building deployments.
Process selected video workloads locally and centralize only what is needed.
Use a controlled local-first architecture where video, data and operations require clear governance.
Camera projects should be sized from real camera models, resolution, codec, bitrate, retention, evidence requirements and the AI workload selected.
The assessment verifies the current camera environment, required storage, local-processing needs and what must synchronize centrally. This prevents unsupported claims about camera counts, storage savings or AI performance.
Camera pricing depends on the number and type of streams, storage retention, selected analytics, gateway profile, installation and support requirements. We do not publish a universal camera price before the readiness assessment.
Start with a camera readiness assessment and one clear operational or security outcome.