WORKFLOX Services
Teach Your Systems to See
Custom Computer Vision, Not Off-the-Shelf APIs
Generic vision APIs work for generic problems. When you need to detect a specific defect, read a specific document format, or track a specific object in your environment, you need a model trained on your data, not someone else's.
The Challenge
Where Off-the-Shelf Vision APIs Fall Short
- ✗Generic object detection doesn't recognize your specific products or defects
- ✗OCR tools fail on non-standard document layouts and Arabic script
- ✗No way to fine-tune accuracy for your exact operating conditions
- ✗Per-request API pricing becomes unsustainable at real volume
- ✗Data never leaves a third-party vendor's servers
Our Approach
What We Build
- ✓Custom-trained object detection models for your specific use case
- ✓Document OCR and data extraction, including Arabic and mixed-script documents
- ✓Real-time video analytics for monitoring, counting, and safety compliance
- ✓On-premise or self-hosted deployment for full data control
- ✓Model evaluation and retraining pipelines to maintain accuracy over time
Use Cases
What We Build For You
Automated Quality Inspection
Detect manufacturing defects, packaging errors, or product inconsistencies in real time using camera feeds on the production line.
Document OCR & Data Extraction
Extract structured data from invoices, ID documents, and forms, including Arabic and bilingual documents, straight into your database.
Retail & Inventory Vision
Shelf monitoring, stock counting, and planogram compliance using in-store or warehouse camera feeds.
Property & Construction Site Monitoring
Track construction progress, detect safety violations, and monitor site security using existing CCTV infrastructure.
Vehicle & License Plate Recognition
Automated access control, parking management, and logistics tracking using license plate and vehicle detection models.
Medical & Diagnostic Imaging Support
Assistive vision models for healthcare providers to flag anomalies in imaging data for human radiologist review, never a replacement for clinical judgment.
Our Process
Step-by-Step Development Process
01
Use Case Definition & Data Audit
We define the exact detection or extraction task and assess what training data already exists versus what needs to be collected.
02
Data Labeling & Preparation
We label and structure your training data, using existing labeled sets where possible to reduce cost and timeline.
03
Model Training & Evaluation
We train or fine-tune the model and evaluate it against real-world test data, not just the training set.
04
Integration & Deployment
We integrate the model into your existing camera feeds, document pipelines, or applications, on-premise or cloud as required.
05
Monitoring & Retraining
We set up accuracy monitoring and a retraining pipeline so the model stays accurate as conditions change.
Technology
Our Stack
Vision projects start with your images, not a model. We collect a labelled sample, train and evaluate several approaches against it, and deploy the one that meets your accuracy target on your own hardware or cloud.
FAQ
Frequently Asked Questions
How is custom computer vision different from using Google Vision or AWS Rekognition?
Off-the-shelf APIs are trained on general-purpose datasets and struggle with domain-specific tasks: a specific defect type, a proprietary document format, or objects unique to your industry. We train or fine-tune models on your own labeled data for meaningfully higher accuracy.
Can you build OCR for Arabic documents?
Yes. We build OCR pipelines that handle Arabic script, mixed Arabic-English documents, and non-standard layouts common in GCC business documents: government forms, invoices, and contracts.
Do you need a large dataset to train a custom vision model?
Not always. We use transfer learning on pre-trained models, which requires far less labeled data than training from scratch. For most business use cases, a few hundred to a few thousand labeled examples is enough to get production-grade accuracy.
Can computer vision models run on-premise for data privacy?
Yes. We deploy vision models on your own infrastructure or edge devices when data cannot leave your premises: common for healthcare, government, and security use cases.
How much does custom computer vision development cost?
A focused single-use-case model (e.g., defect detection or document OCR) typically costs $7,000–$17,000. Multi-camera, real-time video analytics systems range $17,000–$41,000 depending on scale.
How do you hire computer vision developers through WORKFLOX?
On a dedicated model inside your team or a project model against a fixed scope. You interview the specific engineers, and every engagement begins with a labelled evaluation set from your own imagery or documents, so model accuracy is measured on your data before anything ships.
Custom Computer Vision Development in USA, UAE & Saudi Arabia
WORKFLOX builds computer vision systems trained on your own data, not generic pre-trained APIs. From manufacturing quality inspection to Arabic document OCR, we engineer vision models that handle the specific conditions of your business, deployed on-premise or in the cloud depending on your data privacy requirements.
Arabic OCR & Document Intelligence for the GCC
Standard OCR tools consistently underperform on Arabic script and mixed-language business documents common across the GCC. We build custom OCR and document extraction pipelines specifically tuned for Arabic, bilingual, and non-standard document layouts.
Hire Computer Vision Developers: What to Test For
Companies that hire computer vision developers usually need one of three things: a detection or classification model trained on their own imagery, document understanding that turns scans into structured data, or a real-time pipeline over video from cameras or drones. The skills differ, and so does what to test for. For model work, ask how the candidate handles a training set that is small, imbalanced or badly labelled, because yours will be, and how they measure performance beyond accuracy. For document understanding, ask about layout variation and the confidence-threshold review queue; our OCR and document-understanding work routes low-confidence extractions to a person rather than into the system. For real-time pipelines, ask about latency, edge deployment and what happens when a camera drops. We place computer vision developers on both dedicated and project models, and every engagement starts with a labelled evaluation set from your own data so accuracy is measured rather than promised. Our data annotation services handle the labelling that most CV projects underestimate.
Industries We Serve
Industry-Specific Expertise
Next step
Scope a Computer Vision Project
Send a sample of the images or video and the decision you want automated. We will tell you what accuracy is realistic before you commit.
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