AI-powered supply chain visibility, route optimisation, and predictive operations.
48 hrs
advance delay prediction window
5TB+
operational data unified
5
source systems consolidated
Our Experience
We build logistics technology that turns operational data into decisions. From predictive delay models and real-time shipment tracking to dispatcher dashboards and field team mobile apps, we help logistics operators stop reacting to disruptions and start preventing them — with measurable impact on delivery SLAs and fuel costs.
We build logistics technology for carriers, 3PLs, freight forwarders, and shippers across Southeast Asia. Most operations already have the data they need — it is just scattered across an ERP, a GPS feed, a warehouse system, and a spreadsheet someone maintains by hand. We unify it first, then use it to move teams from reacting to disruptions to preventing them.
What We Solve
Most logistics operations have data in five or more systems — ERP, GPS, WMS, weather feeds, and manual logs. We build data engineering pipelines that unify these sources into a single operational picture.
Dispatchers who react to disruptions after they happen carry the penalty costs. We train custom ML models on your own operational history that flag likely delays up to 48 hours in advance, so the reroute happens before the deadline does.
Southeast Asian last-mile delivery involves dense urban areas, high return rates, and diverse payment preferences. We build mobile apps and route engines that optimise for real delivery conditions.
Connecting to suppliers, 3PLs, and port authorities via disparate APIs and EDI formats is a major engineering challenge. We build integration layers that normalise data regardless of upstream format.
What We Build
One operational picture assembled from every system that holds a piece of it, current enough to act on.
Machine learning that gives dispatchers a usable head start instead of an after-action report.
Planning that accounts for the constraints crews actually work under, then adapts when the day changes.
Tools built for one hand, poor signal, and a long shift — the conditions field teams are in every day.
Connecting the parties outside your four walls without forcing every one of them onto your stack.
Why CMMB
Reporting that tells you a shipment was late is not worth much. We train models that flag likely delays around 48 hours ahead with strong accuracy, which is far enough out that a dispatcher can still do something about it.
ERP, GPS, WMS, weather feeds, and manual logs each hold part of the picture and none hold all of it. We build the data engineering layer before the intelligence layer, because models trained on a partial view produce confident and wrong answers.
Route engines and driver apps designed around the conditions crews actually face — imprecise addressing, traffic patterns, failed delivery attempts, cash handling, and returns — rather than an idealised network.
Suppliers, 3PLs, and port authorities arrive with modern APIs, legacy EDI, flat files, or a portal and no integration at all. We normalise upstream data regardless of what shape it turns up in.
Where We Deliver
We deliver from Malaysia on GMT+8, across a region where last-mile is genuinely harder than the textbooks assume: dense urban addresses that maps get wrong, high cash-on-delivery and return rates, and port and customs processes that differ by country. We build for those conditions rather than optimising for a delivery network that does not exist here.
Standards We Build Against
Featured Case Study
Insights
Other Industries We Serve
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