Computer Vision That
Sees What Matters
Computer vision is powerful — but only when the problem is well-defined, the data pipeline is solid, and the model is designed for your real-world conditions. We advise teams on the full lifecycle from feasibility to production-grade deployment.
Advisory Deliverables
Technical artefacts that de-risk your computer vision investment.
Feasibility Assessment
Honest evaluation of whether computer vision can solve your problem given your data, environment, and accuracy requirements.
System Architecture Design
End-to-end pipeline design covering data capture, preprocessing, model inference, post-processing, and integration.
Model Selection & Training Strategy
Recommendation on model architectures, transfer learning approaches, and training data requirements for your use case.
Data Pipeline Design
Annotation strategy, data augmentation plan, and pipeline architecture for continuous model improvement.
Edge Deployment Blueprint
Hardware selection, model optimisation (quantisation, pruning), and deployment architecture for edge inference scenarios.
Evaluation & Monitoring Framework
Test dataset design, accuracy metrics, drift detection, and alerting for production model performance.
Our Advisory Process
Problem Definition
We work with your team to precisely define what the vision system needs to detect, classify, or measure — and under what real-world conditions.
Data Assessment
We evaluate your existing image and video data, identify gaps, and design a data collection and annotation strategy.
Feasibility Prototyping
A quick prototype on a data sample to validate that the problem is solvable at the accuracy level your use case demands.
Production Architecture
We design the complete system including data pipelines, model serving infrastructure, and integration with your existing systems.
Deployment & Monitoring Plan
Hardware recommendations, model update strategy, performance monitoring, and drift detection for long-term reliability.
Computer Vision Is Not Magic — It Is Engineering
Let us help you determine feasibility, design the architecture, and plan the deployment before you commit major resources.
Schedule Free ConsultationWho This Is For
Teams solving real-world visual recognition and inspection challenges.
Manufacturing & QC Teams
Deploy visual inspection systems for defect detection, assembly verification, and quality control on production lines.
FinTech & Insurance
Build document processing pipelines for KYC verification, claims processing, and cheque recognition with high accuracy.
Healthcare & Diagnostics
Design medical imaging analysis systems for radiology, pathology, and dermatology with clinical-grade accuracy requirements.
Automotive & Logistics
Implement vehicle inspection, warehouse automation, and package sorting systems with real-time inference requirements.
Why OpenMalo for Computer Vision
We know the gap between a research demo and a production vision system — and how to bridge it.
Explore Computer Vision for Your Use Case
Describe what you want to detect, classify, or measure. We will assess feasibility and outline an approach.
Visual Inspection Catches 99.1% of Defects
Electronics Manufacturer Automates Quality Control
An electronics manufacturer was relying on manual visual inspection for PCB defect detection. Human inspectors caught only 82% of defects and throughput was a bottleneck.
The Challenge
Manual inspection was slow, inconsistent, and missing critical defects that resulted in costly returns and customer complaints.
Our Approach: We started with a controlled lighting and camera positioning design, then built a multi-stage detection pipeline: a fast screening model for obvious defects and a high-accuracy model for borderline cases. The system was deployed on edge GPUs at each inspection station with a dashboard for quality engineers to review flagged items.
Frequently Asked Questions
It varies by complexity. Simple binary classification may need 500-1000 images per class. Complex multi-class detection typically needs 2000+ annotated images. We design augmentation strategies to maximise limited data.
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