Client Experiences
Feedback from organizations that have implemented computer vision solutions with our support
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Honest feedback about working with lumiinoaiis
Karthik Tan
Operations Manager
Penang, Malaysia
The feasibility study was valuable because it gave us realistic expectations before we committed budget. They identified specific lighting conditions in our facility that would affect accuracy and suggested practical adjustments. The candid assessment saved us from investing in something that wouldn't have worked well in our environment.
January 28, 2026
Nur Izzah
Quality Manager
Shah Alam, Malaysia
Model training took longer than initially estimated due to some data quality issues we had not anticipated. However, they were transparent about the challenges and worked with us to improve our image collection process. The final model performs reliably for our defect detection needs, and the documentation helps our team understand when retraining might be needed.
February 5, 2026
Wei Liang
IT Director
Kuala Lumpur, Malaysia
Integration into our document processing workflow went smoothly. They took time to understand our existing system architecture and designed the API accordingly. The structured testing phase let our team validate behavior before full deployment, and the support period afterward was helpful for addressing minor adjustments we needed.
January 18, 2026
Ahmad Mukhtar
Production Lead
Johor Bahru, Malaysia
What I appreciated was their willingness to explain technical details without overwhelming us with jargon. They presented validation results clearly and discussed edge cases where the system might struggle. This helped us set appropriate expectations with our team about what the vision system could and could not detect.
January 22, 2026
Siti Choong
Logistics Coordinator
Petaling Jaya, Malaysia
The model they developed for our package counting application works well even during peak hours with varying lighting. They optimized it to run on our existing camera hardware rather than requiring expensive upgrades. Documentation included clear guidance on monitoring performance and when model updates might improve accuracy.
February 2, 2026
Raj Kumar
Technical Manager
Ipoh, Malaysia
They provided realistic timelines from the start and met them consistently. Regular updates during development kept us informed about progress and any issues encountered. The knowledge transfer sessions helped our engineers understand the system well enough to handle basic troubleshooting and monitoring without constant vendor involvement.
January 15, 2026
Success Stories
Detailed examples of computer vision implementations
Manufacturing Quality Control
Industry: Electronics Manufacturing
Location: Selangor
Timeline: 8 weeks development + 3 weeks integration
Challenge
Manual visual inspection of circuit boards was time-consuming and subject to operator fatigue, leading to inconsistent defect detection rates. The facility needed automated quality control that could maintain accuracy across shifts without requiring expensive specialized equipment.
Solution
Developed custom defect detection model trained on facility's actual production data, accounting for specific lighting conditions and board variations. Optimized for existing industrial cameras to avoid hardware replacement. Integrated with production line reporting system.
Results
Defect Detection Accuracy
Inspection Time Reduction
Stable Operation Without Issues
"The system has been reliable since deployment. Our quality team now focuses on investigating flagged items rather than examining every board, which is a better use of their expertise."
Document Processing Automation
Industry: Financial Services
Location: Kuala Lumpur
Timeline: 6 weeks development + 2 weeks integration
Challenge
Processing high volumes of customer identity documents required manual data entry and verification, creating bottlenecks during peak periods. The organization needed automated extraction that could handle various document formats and qualities while maintaining accuracy for regulatory compliance.
Solution
Built document text extraction model capable of handling common Malaysian identity documents in various conditions. Implemented confidence scoring to flag uncertain extractions for manual review. Integrated with existing workflow management system via secure API.
Results
Auto-Processing Rate
Processing Time Decrease
Extraction Accuracy (High Confidence)
"The confidence scoring feature works well. Items flagged for review genuinely need human attention, while high-confidence extractions are consistently accurate. This has significantly reduced our processing backlog."
Inventory Management System
Industry: Retail Logistics
Location: Klang Valley
Timeline: 5 weeks development + 4 weeks integration
Challenge
Warehouse inventory counting was labor-intensive and prone to human error, particularly during stock audits. The operation needed automated counting that could work with existing shelving layouts and varying product sizes without requiring infrastructure changes.
Solution
Trained object detection and counting model using warehouse imagery across different lighting conditions and viewing angles. Optimized for tablet deployment enabling mobile counting. Developed offline capability for areas with poor network coverage.
Results
Audit Time Reduction
Counting Accuracy
Return on Investment Period
"Being able to count inventory with tablets rather than clipboards has made audits much faster. The offline capability was essential given our warehouse network coverage. Staff adapted to the system quickly."
Project Statistics
Overall metrics from our computer vision practice
Completed Projects
Since establishment in 2024
Client Satisfaction
Based on post-project surveys
Industry Sectors
Manufacturing, logistics, finance, retail, healthcare
Average Accuracy
Across deployed models
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Whether you're exploring computer vision for the first time or seeking support for an existing project, we're available to discuss your requirements and recommend an appropriate approach.