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Agentic video search

Find the moment without scrubbing.

Describe a person, object, place, action, or event in plain language and review the matching camera evidence where search is configured.

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Person in a blue jacket moving through a monitored branch
AI layer activeAgentic video search

Search the camera archive

Ask the operation. See the evidence.

Type a question, run the search, and inspect the camera sightings that match the configured index. The demo uses representative branch footage to show the interaction pattern.

Agentic video searchNatural language over compatible camera streams
AI agent active

Describe a person, object, place, or event in plain language.

6 matching sightings found

Person in a blue jacket moving through a monitored branch98% match
Camera 01Context attached
Person in a blue jacket moving through a monitored branch96% match
Camera 02Context attached
Person in a blue jacket moving through a monitored branch93% match
Camera 03Context attached
Person in a blue jacket moving through a monitored branch91% match
Camera 04Context attached
Person in a blue jacket moving through a monitored branch88% match
Camera 05Context attached
Person in a blue jacket moving through a monitored branch86% match
Camera 06Context attached

Search is more useful with context.

A result should help an operator decide what to review next. A similarity score alone is not enough.
01

Natural-language intent

Describe the visual detail or action you want to find instead of guessing a camera first.

02

Time and site scope

Narrow a question by date, time, site, or selected cameras where those filters are available.

03

Relevant sightings

Review matching clips or frames with camera and timestamp context attached.

04

Evidence review

Select the result worth sharing, exporting, bookmarking, or passing into the next workflow.

A practical boundary

Search works from what the system can index.

Semantic video search depends on the available footage, stream access, analytics or embeddings, retention, and the quality of the visual signal. AEyeTech should validate those conditions with representative sources before promising a specific search experience.

The goal is faster investigation and better evidence. No model can recover every detail from every recording.

Search one real archive

Show us the moment your team cannot find fast enough.

We will help determine whether the streams, metadata, and workflow are suitable for a proof of concept.
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