What Organizations Say About Working With Us
Feedback from Malaysian companies that have engaged with us on computer vision projects, strategy planning, and data preparation.
Back to HomeClient Experiences
Here's what organizations have shared about their projects with Synthara.
Khairul Lim
Operations Manager, Kuala Lumpur
The computer vision system they built for our quality control process has been running smoothly for three months now. What impressed us most was how they took time to understand our production environment before suggesting approaches.
January 28, 2026
Sarah Yap
Technology Director, Penang
The strategy workshop helped our team get aligned on which AI projects to pursue first. The facilitators were patient with our questions and provided realistic assessments of what would be involved in each use case.
February 5, 2026
Ravi Ahmad
Project Lead, Selangor
We engaged them for data labelling services and the quality was consistently high. They set up clear protocols at the beginning and caught several edge cases that we hadn't considered in our original taxonomy.
January 15, 2026
Mei Lin Tan
Supply Chain Manager, Johor
Their approach to our warehouse monitoring project was thorough and methodical. They provided regular updates and were transparent when challenges came up. The documentation they delivered helps our IT team maintain the system.
February 2, 2026
Azman Kadir
Manufacturing Head, Melaka
We appreciated their honesty about what could be achieved within our budget and timeline. They suggested starting with a smaller proof-of-concept rather than committing to a full system immediately, which helped us validate the approach before scaling.
January 22, 2026
Jennifer Chen
Retail Operations, Petaling Jaya
The shelf monitoring system they developed handles our product inventory tracking more reliably than our previous manual checks. Integration with our existing systems went smoothly, and they stayed available during the initial deployment period.
February 9, 2026
Success Stories
Detailed accounts of how organizations have applied AI solutions to address specific operational challenges.
Challenge
A manufacturing facility needed to inspect components for surface defects but manual inspection was inconsistent and time-consuming. Their quality assurance team was spending four hours daily on visual checks.
Solution
We developed a computer vision system that automated defect detection using cameras positioned at key inspection points. The system was trained on their historical defect data and integrated with their existing production line management software.
Results
Inspection time reduced by 72%, allowing quality assurance staff to focus on complex cases. Defect detection consistency improved, with the system maintaining 94% accuracy over a three-month validation period.
"The system has been running reliably since deployment. The team provided excellent support during the transition period." — Operations Manager, Electronics Manufacturing
Challenge
A logistics company wanted to explore AI for route optimization and demand forecasting but their leadership team had limited technical background. They needed help identifying practical starting points.
Solution
We facilitated a two-day strategy workshop that covered AI capabilities, evaluated potential use cases, and assessed feasibility. The workshop included hands-on demonstrations with sample data from their operations.
Results
The team identified three viable projects and developed a phased implementation plan. They had clarity on resource requirements and realistic timelines. Two months after the workshop, they initiated their first pilot project.
"The workshop helped us move from vague interest in AI to concrete plans we could actually execute." — Technology Director, Logistics Services
Challenge
A retail chain had collected thousands of product images but needed them labeled for training a recommendation system. Their internal team lacked bandwidth to handle the annotation workload.
Solution
We established a managed annotation pipeline with clear quality standards and multi-annotator consensus protocols. Regular progress reports ensured the client stayed informed throughout the three-week labelling period.
Results
Delivered 8,500 labeled images with 96% quality score on validation checks. The client used this dataset to train their recommendation model, which improved product discovery on their e-commerce platform.
"The labelling quality was consistently high, and they were responsive when we needed taxonomy adjustments midway through." — Product Manager, Retail Technology
By the Numbers
Quantitative indicators of our project delivery and client relationships.
Projects Completed
Across manufacturing, retail, and logistics sectors
Satisfaction Rate
Organizations rating experience as satisfactory or better
Years Experience
Combined team expertise in AI development
Average Timeline
Months from project start to deployment
Contact Information
Reach out to discuss your project or learn more about our services.
Location
7 Jalan Sultan Ismail
Kuala Lumpur
Interested in Working Together?
Whether you have a specific project in mind or would like to explore what might be possible, we're happy to have a conversation.