What Our Clients Say
Feedback from teams who have worked with us on supply chain optimization, image recognition, and AI project scoping.
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Haziq Taufik
Supply Chain Manager, Penang
We engaged kalibrena for supply chain optimization and the result was a practical model our planning team actually uses daily. They took the time to understand our constraints — seasonal fluctuations, multiple warehouse locations — before building anything. The scenario tool they delivered gives us flexibility we did not have before.
22 January 2026Michelle Wong
QA Director, Shah Alam
The image recognition model kalibrena built for our defect detection line has been running for three months now with strong accuracy. The labelling guidelines they provided mean our team can continue improving the model internally. I wish the training phase had been a bit shorter, but the thoroughness shows in the output quality.
18 January 2026Raj Subramaniam
COO, Kuala Lumpur
We did the scoping workshop before committing to a larger project. It was exactly what we needed — a clear, honest assessment of what was feasible with our data and a project brief we could take to our board. The workshop paid for itself in clarity alone.
30 January 2026Nurul Zahra
Logistics Lead, Johor Bahru
What stood out about kalibrena was their honesty during the scoping phase. They told us upfront that one of our data sources was not reliable enough for the model we had in mind, and suggested a different approach that worked with what we had. That kind of candour saved us from a costly mistake.
5 February 2026Chong Li Wei
IT Manager, Petaling Jaya
The documentation quality from kalibrena was noticeably better than what we have received from other vendors. Everything from the model architecture to the maintenance procedures was written so our internal team could understand it. We have been running the image classification system for two months without needing external support.
28 January 2026Aiman Shah
Head of Operations, Cyberjaya
The supply chain optimization model kalibrena built has helped us reduce overstock in two of our highest-cost categories. It was not a dramatic overnight change but rather a steady improvement over weeks as our team learned to work with the scenario recommendations. That is exactly the kind of realistic outcome I was hoping for.
10 February 2026Success Stories
Supply Chain Rebalancing for a Regional Distributor
Challenge
A distribution company operating across Peninsular Malaysia was experiencing consistent overstock in some warehouses and stockouts in others. Manual rebalancing was time-consuming and reactive rather than preventive.
Solution
kalibrena built an optimization model that factored in regional demand patterns, lead times between facilities, and storage cost differentials. The model produced weekly scenario recommendations for inventory rebalancing, integrated into the client's existing dashboard.
Results
Within eight weeks of deployment, the client saw a 17% reduction in excess inventory holding costs and a 24% improvement in stockout frequency. The planning team now runs scenarios weekly as part of their standard workflow.
"The model gave us confidence in decisions we were already making intuitively — and caught patterns we would have missed." — Operations Manager
Visual Inspection Automation for Electronics Manufacturing
Challenge
An electronics manufacturer in Penang needed consistent defect classification across production shifts. Human inspection was thorough but slow, and classification consistency varied between inspectors and shifts.
Solution
kalibrena trained a custom image recognition model on the client's defect image library, defining six classification categories with the QA team. The model was deployed as an inference API that integrated with the existing inspection workflow as a secondary check.
Results
The model achieved 94.2% classification accuracy on the test dataset. Inspection throughput improved by 30% with the model flagging defects for human review. Cross-shift consistency improved significantly based on the client's internal tracking.
"The labelling guidelines were the surprise value — our team can keep improving the model without external help." — QA Lead
AI Readiness Assessment for a Retail Chain
Challenge
A mid-sized retail chain in KL wanted to use AI for demand forecasting but was unsure about data quality, technical feasibility, and the investment required. Previous vendor conversations had been unclear about scope.
Solution
Through the AI Project Scoping Workshop, kalibrena assessed the client's existing POS data, identified gaps in their data pipeline, and defined a phased approach starting with a pilot across three store locations before scaling.
Results
The client received a detailed project brief with realistic timelines and budget estimates. They used the brief to secure internal approval and subsequently engaged kalibrena for the pilot phase with clear expectations on both sides.
"The workshop gave our board the confidence to invest in AI because the scope and risks were clearly laid out." — Director of Strategy
By the Numbers
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