Lorong AI takes machine learning into live operations. We build the data pipelines, train the models, connect them to your systems, then watch their performance after launch.
Start with an operational problem that already has a measurable cost. We select the technique, data inputs and deployment shape around that problem.
Sensor streams from CNC machines reveal unusual vibration, temperature and cycle behaviour before a line stops in Jurong.
Explore factory applicationsTransaction models flag unusual behaviour while preserving a clear review path for fintech and financial services SMEs.
See financial services solutionsProduct, content and offer recommendations respond to customer intent across Singapore e-commerce and media platforms.
Explore customer analyticsCamera feeds help logistics teams check loading zones, package movement and safety conditions in warehouses and properties.
Discuss a vision use caseA plant team had years of CNC sensor readings, yet maintenance decisions still depended on fixed intervals and operator instinct. Lorong AI joined the signals, maintenance records and production context into one modelling pipeline.
An anomaly detection model now scores machine behaviour at the edge. Only the useful summaries move to the cloud, where the operations team reviews trends and retraining performance.
"The useful change was operational. Our engineers see a reason to inspect a machine before the alarm becomes a stoppage."
The first launch is a checkpoint, not the finish line. Our MLOps setup gives your team an accountable way to see when data changes, predictions weaken or a model needs attention.
Package the model and its dependencies so the same service behaves consistently across Python, Azure, AWS or an on-premise edge environment.
Test features, code and model versions before release. Every production change has a traceable approval path.
Track latency, data quality, prediction distribution and business outcomes rather than relying on a single accuracy score.
Set practical thresholds for drift, seasonality and new behaviour. Retraining starts with evidence, then passes through review.
Give risk, compliance and operations teams a plain account of why a prediction was made and where human judgement remains required.
Different business questions need different model behaviour. These two implementations show how we connect technical choices to a commercial measure.
A recommendation engine ranked products using browsing signals, purchase history and catalogue relationships. The team tested placements in controlled releases and recorded an 18% revenue lift.
A fraud detection model combined transaction patterns with device and account context. Review queues became more focused, with a 70% reduction in false positives.
Bring one process, one data source and one decision that matters. In a 90-minute feasibility workshop, we'll define a high-confidence pilot, its baseline metric and the path to production.