Innovation
Phones, drones and cameras already record most of what happens on a site, in a plant or at a service counter. Computer vision turns those pictures into data a business can use: what is there, what has changed, what is faulty and what needs a person to look.
Quick answer
Computer vision is AI that interprets images and video automatically. Next Orbit builds vision systems that detect objects, read text, compare plan against actual progress, find defects and flag unsafe conditions from photos, scanned documents and camera feeds. We deliver them for construction, energy, manufacturing, logistics, healthcare and retail organisations in Dubai, Abu Dhabi, Sharjah, the entire UAE, the GCC and globally that still rely on people for routine visual checks.
Every engagement is architecture-led, delivered by an innovative, R&D-backed team of security-first engineers and quality-controlled before each release, for clients across Sharjah, Dubai, Abu Dhabi and the entire UAE, the GCC and globally.
What you get
AI that reads photos, video and documents to check progress, quality and safety, from site images to live cameras.
6 experiments, all running in production
Experiment 01 · Computer vision
Field photos are compared with the plan to confirm that claimed work is visible.
MeezanX, the EPC project controls platform Next Orbit implements, uses AI-validated plan-versus-actual photos before a progress claim enters the approval chain.
Experiment 02 · Computer vision
Models trained on good and faulty examples flag surface defects, missing parts and assembly errors for an inspector to confirm.
Experiment 03 · Computer vision
Video analysis detects missing protective equipment, entry into restricted zones and other conditions your HSE team defines.
Experiment 04 · Computer vision
OCR and layout models pull fields from invoices, delivery notes, IDs and forms, in Arabic and English.
Experiment 05 · Computer vision
People, vehicles, pallets and stock are counted or measured from images, replacing clipboards and manual tallies.
Experiment 06 · Computer vision
When bandwidth or privacy demands it, the model runs on a device at the site and only results leave the premises.
Need computer vision for a project that does not fit a template? Tell us the problem and one of our expert engineers will map it to an architecture.
Discovery CallA computer vision system receives an image or a video frame and returns an answer: this worker is wearing a hard hat, this weld looks irregular, this invoice is missing a VAT number, this area has changed since last week.
Modern systems learn from labelled examples instead of hand-written rules, and large pre-trained models mean useful accuracy often needs far fewer examples than it did a few years ago.
The value comes from consistency at scale.
A model can review every photo, every frame and every page without tiring, and apply the same standard each time. People stay responsible for judgement calls; the system points their attention to the cases that need it.
We begin with a precise question and a sample of your real images.
Vision projects succeed or fail on data: the lighting, angles, camera quality and variety of your actual environment. We review what you already capture and, where necessary, define how photos should be taken so the model receives usable input.
Next we build a proof of concept on that data with an agreed measure of success, such as the share of defects caught and the rate of false alarms.
Only when the proof of concept meets that measure do we engineer the production system: the capture app or camera integration, the model service, the review screen and the links to your existing software. Our R&D-led approach means the risky part is tested first, while it is still inexpensive to change course.
Ask three questions.
Is the visual check frequent enough to justify automation? Can images be captured consistently? What does a wrong answer cost? A system that occasionally misses a cosmetic mark is very different from one that informs a safety decision, and the design, the review step and the acceptable error rate should reflect that difference.
Privacy matters too, because video of people is personal data.
We design for it: processing on site where appropriate, storing as little as possible, restricting access and working with your advisers on the rules that apply. No vision model is perfect, so we report accuracy measured on your own data rather than quoting a figure in advance.
How we work
5 stages, each with a deliverable you can review and sign off before the next begins.
Readiness level 1 of 5 · Idea
The visual task, the decision it supports and the measure of success are agreed.
Readiness level 2 of 5 · Proven
Sample images are gathered and reviewed, and labelling rules are written.
Readiness level 3 of 5 · Piloted
A working model is evaluated on your real data against the agreed measure.
Readiness level 4 of 5 · Deployed
Capture, model service, review workflow and integrations are built and tested.
Readiness level 5 of 5 · Operating
Accuracy is tracked in live use and the model is retrained as conditions change.
Tools and standards
Tools are chosen to suit the project, not the other way round. These are the ones we reach for most often; the final selection is made during solution architecture and explained in your tailored proposal.
Questions, answered
It depends on the task and how varied conditions are. Pre-trained models reduce the number considerably, and we confirm the requirement during a feasibility review using a sample of your own images.
Often, yes, provided the cameras offer a usable stream and the resolution and angle suit the task. We check this before recommending any new hardware.
No. Models can run on edge devices on site so that only alerts and results are sent onward. The choice is made during solution architecture.
Accuracy depends on the task and on image quality. We measure it on your data during the proof of concept and report it plainly, including misses and false alarms.
Yes. MeezanX uses AI to validate plan-versus-actual field photos before progress is approved on EPC projects.
It is scope-based, driven by the task, the data available, any hardware and the integrations. A tailored proposal follows a Discovery Call and a feasibility review.
Pick what you need and the right person on our team replies.
Your reference NO-000000-0000
An engineer will reply to you. Quote the reference if you follow up.
What happens next
Share the problem, the systems involved and your timeline. You will hear back from an engineer, not a sales script.