Voice, Language & Vision
Catch the defect before it leaves your line
A camera on your production line, a model trained on your own products, and an alert the moment something falls outside spec. Built for how your line actually runs, not a generic demo.
2 to 4
weeks to a working first version
24/7
inspection coverage once deployed
2
revision rounds included per stage
The direct answer
Computer vision QC uses cameras and a trained model to inspect parts on your production line and flag defects in real time, without a human inspector checking every unit. It suits manufacturers and packagers in Saudi Arabia who run repetitive visual checks today (scratches, missing labels, misalignment, wrong count) and want that check to run continuously, without fatigue, at line speed.
What this removes.
Inspectors miss defects on long shifts
Today
Human inspectors tire by hour six and let the same defect types slip through on every long shift.
With the system
The camera checks every unit the same way, hour one and hour twelve, and never stops paying attention.
Rework discovered too late
Today
Defects surface at final packing or after a customer complaint, long after the batch is already made.
With the system
The model flags the defect at the station where it happened, so you catch it before ten more units follow it.
No record of what actually shipped
Today
You know a defect got through but can't say when, on which shift, or how many units around it.
With the system
Every inspection is logged with a timestamp and an image, so a recall or a customer claim has an actual trail.
Arabic-speaking line staff shut out of the tooling
Today
The QC software your integrator sold you ships in English only, so your line supervisors never really use it.
With the system
The alerts, the dashboard and the daily report are in Arabic, so the people running the line can act on them directly.
What lands in your hands.
Defect detection model
Trained on images of your own product, not a generic open dataset.
Camera and lighting setup
Positioned and lit for your line, tested against your actual defect rate.
Real-time alerting
Flags a reject at the station, before it moves to the next process step.
Inspection log
Timestamped record of every unit checked, with the image, for audits and claims.
Arabic and English dashboard
Shift-level pass and reject counts, viewable by supervisors on the floor.
Retraining pipeline
A way to add new defect examples as your product or packaging changes.
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.
- Line assessment3 to 5 days
- Data collection and labeling1 to 2 weeks
- Model training and camera setup2 to 3 weeks
- Pilot on the live line2 to 3 weeks
- Full deployment and supportongoing
Line assessment
3 to 5 days
We walk your line, look at your current defect types and rates, and confirm computer vision is the right tool before we scope anything.
- Walk the line with us
- Share current reject-rate data
- Name the top 3 to 5 defect types
- Line and lighting assessment
- Feasibility note per defect type
- Scoped stage-by-stage plan
We move on when we agree on which defects are in scope and where the cameras go.
Data collection and labeling
1 to 2 weeks
We capture images of your product, good and defective, and label them so the model has something real to learn from.
- Provide sample units, good and bad
- Give floor access for image capture
- Confirm defect definitions
- Labeled image dataset
- Data collection setup on your line
- Baseline defect-count report
We move on when we have enough labeled examples of each defect type to train against.
Model training and camera setup
2 to 3 weeks
We train the detection model on your data, mount and calibrate the cameras, and test detection accuracy against real units running at line speed.
- Run the line at normal speed for testing
- Flag any missed or false detections
- Trained detection model
- Mounted and calibrated cameras
- Accuracy report by defect type
We move on when detection accuracy on your test batch clears the threshold we agreed at stage one.
Pilot on the live line
2 to 3 weeks
The system runs alongside your existing inspection process so you can compare its calls against your current method before it takes over.
- Run the pilot in shadow mode
- Review flagged rejects with your QC lead
- Live pilot deployment
- Daily comparison against manual inspection
- Tuning based on pilot results
We move on when your QC lead signs off that the pilot's calls match or beat manual inspection.
Full deployment and support
ongoing
The system runs on the live line, we monitor uptime and accuracy, and we retrain the model as your product or packaging changes.
- Use the dashboard for shift handovers
- Flag new defect types as they appear
- Full production deployment
- Uptime and accuracy monitoring
- Scheduled model retraining
We move on when the system is catching defects on every shift and your team is acting on the dashboard without us.
Asked before signing.
How is this priced?
Pricing is scoped per line, based on number of camera stations, defect types in scope, and whether we're training a new model or extending one. We quote after the line assessment in stage one, not before we've seen the line. Ongoing support after deployment is a separate monthly line item.
What if our defect rate is too low to have enough training images?
This comes up often on well-run lines. We can supplement real defective units with synthetic variations of your good units, and we start the model on a smaller set and improve it as more real defect images come in during the pilot. It slows stage two slightly but doesn't block the project.
Does the dashboard work for our Arabic-speaking line supervisors?
Yes. The dashboard, alerts and daily reports run in Arabic and English, and we set the default language per user during setup. This is not a translation layer bolted on afterward, it's built bilingual from stage one.
Where does the video data go, and is it PDPL compliant?
Inspection images are of product units, not people, and we design camera placement to avoid capturing staff faces where the layout allows it. Data storage and processing follow PDPL requirements, and we document exactly what's captured, where it's stored, and for how long, before deployment.
Can this integrate with our existing MES or ERP system?
Yes, reject counts and inspection logs can push into most MES and ERP systems through their standard APIs, including Odoo and SAP. We scope the specific integration during the line assessment since every plant's setup is a little different.
Show us your line, we'll tell you if this fits
Bring your current defect rate and a few sample units. We'll tell you honestly whether computer vision QC is worth building for your line, and what it would take.