Computer Vision / AI video analysis
Question recorded video in plain language, follow up like in a chat, and jump to the frames behind every answer.
From Dubai, VBTech builds LLM-powered video analysis for operations, security and engineering teams across the UAE, Saudi Arabia and the GCC: searchable scenes, a chronology of events and verifiable answers, integrated with the tools you already use.
Runs on-premise or in the cloud: your footage and the models can stay on your own servers.
Try it below on a real industrial fire recording, in English or Arabic. No sign-up needed to start.
What happened before the vehicle entered the loading area?
Live demo / real recording
This drone recording of a real industrial fire response is already indexed. Ask what happened, how many firefighters and vehicles are on site and where, then follow up: the assistant keeps the conversation in mind, answers in English or Arabic, and every timecode or frame takes the player to that moment.
Click a scene to jump to it. The moments behind each answer appear here.
Ask about this video
Ask me about this drone recording of an industrial fire response. I answer from the video index, count people and vehicles, remember our conversation for follow-up questions and give timecodes you can click to jump to the moment.
0 / 600 · Enter to send, Shift+Enter for a new line. Follow-up questions keep the context of this conversation. Answers come from indexed frames only (no audio). Answers can be wrong: check the timecodes. Do not include personal or confidential information.
Bring your use case, footage and systems into a focused discussion.
From footage to understanding
A detection tells you something is there.
Context helps you understand what happened.
LLM-powered video analysis combines visual descriptions with a language model to make recorded footage easier to explore. Scenes, visible activity and changes become an index your team can search and question.
Every answer stays tied to timecodes and frames, so a reviewer checks the footage instead of trusting a summary.
How it works
Three steps turn a recording into something your team can question.
Start with what is visible
The analysis separates camera shots and describes sampled frames: setting, objects, visible text and activity. Changes are interpreted within their scene rather than assuming every cut shows the same place.
See the sequence
Explore a chronological view of the recording. Use timecodes and annotated frames to review an event, inspect a change and understand what preceded it.
Ask, then check
Ask in plain language, then follow up. The assistant draws on the scene context, the chronology and the most relevant annotated frames, keeps the conversation in mind and answers with timecodes and frames to check.
Built around the decision
Start with what a reviewer needs to understand, then shape the analysis and the interface around that task.
Review activity at a port terminal, an airport apron, a logistics yard or an oil & gas site: a vehicle movement, a loading operation, a handover. Find the relevant moments before investigating the detail.
“What changed during this sequence?”
Search security or site camera recordings for the moments that matter and see what preceded them. The reviewer gets context and evidence rather than an isolated alert.
“What happened before the event?”
Turn recordings into descriptions, keywords and a chronology. Help engineering and operations teams navigate material that is difficult to search manually.
“Where does this activity appear?”
Illustrative use cases. The analysis supports human review; it does not certify compliance or replace a safety system.
Beyond the demo
A useful application needs more than a model response. We build the interfaces and integrations that make video analysis usable by your team.
Explore our Computer Vision services ↗Search, scene context, event chronology and evidence views built around the decisions your users make.
Extend the application with your incident tools, asset context, dashboards or reporting workflow through the available interfaces.
Run the models, the video index and the application on your own servers, or in a hosted environment when confidentiality allows. Access, retention, processing capacity and monitoring are defined around your environment.
A clear starting point
LLM video analysis uses vision-language models to describe the scenes and sampled frames of a video, stores those descriptions as a searchable index, then lets a language model answer questions over that index. Answers cite timecodes, so a reviewer can check each one against the original frames.
Yes, once the recording is indexed. Instead of scrubbing through hours of video, the reviewer asks for an activity, a vehicle or an event and gets the matching moments with timecodes and frames. Follow-up questions narrow the search, as in the demo on this page.
No. This experience works on recorded video that is indexed first. Live camera detection and alerting are a different workflow, which we build as part of our Computer Vision services.
No. The analysis uses sampled frames and scene boundaries. Sampling, image quality, occlusion and model interpretation affect what enters the index, so critical findings must be verified against the original footage.
Yes. The vision-language models, the video index and the application can all run on your own servers, so recordings never leave your network. A hosted model service is an option when confidentiality allows. We size the hardware against your video volume and the response time you need.
Yes. We scope the application around your footage, questions, users and integration points. A representative sample shows what the analysis can recover before a wider rollout.
Yes. VBTech is based in Dubai and works with teams across the UAE, Saudi Arabia and the wider GCC. Depending on your data residency and confidentiality requirements, footage can stay on your own infrastructure with local models, or use a hosted model service. Questions and answers work in English and Arabic.
Use representative recordings and a set of questions with known answers. Review event coverage, timecode relevance, unsupported interpretations and handling of uncertainty, then measure processing time and how much review effort the workflow saves.
Build around your footage
Tell us what your team needs to find, understand or act on.
Let’s discuss your video workflowVBTech · Industrial software · Dubai, UAE