Voice, Language & Vision
Cameras that watch the process, not the people
You already have cameras on the line. We turn that footage into defect counts, throughput numbers and zone alerts that land in a dashboard or a WhatsApp message, so someone acts before the shift ends.
4 to 8
weeks to a working first model
24/7
camera feeds processed, not sampled
2
site visits included in scoping
The direct answer
Industrial video analytics applies computer vision models to camera feeds already installed on your production line, yard or warehouse, to detect defects, count units, flag safety zone breaches, or track equipment status automatically. It is built for manufacturing, logistics and industrial operations teams in Saudi Arabia who need to know what is happening on the floor without stationing someone to watch a screen. It is not a surveillance product and does not track individual workers.
What this removes.
Defects caught too late
Today
A bad batch runs for an hour before someone on the next station notices and flags it.
With the system
The model flags the defect at the point it happens, with a snapshot attached, within seconds.
Manual counting eats hours
Today
Someone tallies units off a clipboard or rewatches recorded footage to reconcile a shift count.
With the system
Line counts update automatically and reconcile against your ERP figures at shift close.
Safety zones rely on signage
Today
A restricted zone near moving equipment depends on a sign and habit, not enforcement.
With the system
A camera-covered zone raises an alert the moment someone or something enters it unexpectedly.
Camera footage sits unused
Today
Recordings exist for insurance and incident review, but nobody watches them in real time.
With the system
The same feeds now generate structured events your team can query and act on daily.
What lands in your hands.
Camera and feed integration
Connects to existing RTSP/IP cameras, no new hardware required in most cases.
Trained detection model
A model tuned on your product, line or zone, not a generic off-the-shelf classifier.
Event dashboard
Live counts, defect log and zone alerts in one screen, in Arabic and English.
Alert routing
Alerts pushed to WhatsApp, email or a screen on the floor, your choice.
Snapshot and clip archive
Every flagged event stored with a timestamped image for audit and review.
Handover documentation
Model scope, retraining process and support contact, in plain language.
Systems and platforms we work with
- OpenAI
- Anthropic
- Google Gemini
- Meta
Systems and platforms we work with
- React
- Next.js
- TypeScript
- Node.js
- Python
- Flutter
- PostgreSQL
- Supabase
- Tailwind CSS
- Docker
- GitHub
- Google Cloud
- Figma
Five stages. You sign off every one.
Read each stage as a small contract: what we need from you, what lands in your hands, and the sentence that has to be true before we move on.
- Site walk and scoping3 to 5 days
- Data collection and labeling1 to 3 weeks
- Model build and testing1 to 3 weeks
- Pilot on the live feed1 to 2 weeks
- Live operation and supportongoing
Site walk and scoping
3 to 5 days
We visit the site, look at your camera coverage and agree exactly what the model needs to detect and how good it needs to be before it goes live.
- Grant site and camera access
- Name the specific defect or event to detect
- A written scope with accuracy target
- A camera coverage map
We move on when we move on when both sides sign off on the scope and target accuracy.
Data collection and labeling
1 to 3 weeks
We pull sample footage from your existing cameras and label the defects, counts or zones the model needs to learn, working from real plant conditions, not stock footage.
- Provide footage access or recordings
- Confirm labeled examples are accurate
- A labeled training dataset
- A data quality report
We move on when we move on when the dataset covers enough real variation to train on.
Model build and testing
1 to 3 weeks
We train the detection model against your labeled data and test it on footage it has not seen, comparing results against the accuracy target from stage one.
- Review test results with us
- Flag any missed or false detections
- A trained model
- A test accuracy report against target
We move on when we move on when test accuracy clears the agreed target on held-out footage.
Pilot on the live feed
1 to 2 weeks
The model runs against a real live camera feed alongside your existing process, so you can compare its flags against what your team catches manually before switching over.
- Run the pilot alongside current process
- Confirm alert routing works as expected
- Live dashboard access
- A pilot performance summary
We move on when we move on when the pilot matches or beats manual detection over the trial period.
Live operation and support
ongoing
The system runs continuously against your camera feeds. We monitor accuracy, retrain the model as your line or product changes, and stay on call for issues.
- Flag drift or new defect types as they appear
- Use the dashboard for daily decisions
- Ongoing monitoring
- Scheduled model retraining
We move on when this is the standing state; we retrain when your process changes enough to need it.
Asked before signing.
Do we need new cameras, or can you use what we already have?
Most plants already have enough camera coverage for at least one use case. We work with existing RTSP or IP camera feeds first and only recommend new hardware if a blind spot genuinely needs it. We will tell you honestly if your current coverage will not support the detection you want before you pay for anything.
How is this priced?
Pricing is scoped per camera feed and per detection type, based on the site walk in stage one. A single defect-detection use case on one line costs less than a multi-zone safety and counting rollout across a facility. You get a fixed quote after scoping, before any build work starts.
Does this track individual workers?
No. The models are built to detect defects, count units, and monitor zones and equipment, not to identify or track people. If a safety use case needs to detect a person entering a restricted zone, it flags the event and location, not who the person is. This is an industrial QC and operations tool, not a surveillance system.
Where does our footage and data live, and is it PDPL compliant?
Footage and derived data stay within infrastructure that meets Saudi PDPL requirements, and we minimize what leaves your site to only what the model needs. Retention periods and access controls are agreed with you during scoping. We can walk your compliance team through the exact data flow before you sign off.
Get eyes on your production line that never blink
Send us your camera setup and the defect or event you want caught. We will scope it and tell you honestly whether video analytics is the right fit before you commit to anything.