Retail
Retail - Demand Forecasting
Inventory levels were based on historical averages, leading to frequent stockouts on trending items.
30% improvement in stock availability. 15% reduction in carrying costs.
View full case studyMachine Learning Services
Historical data is a record. Machine learning makes it a forecast.
Machine Learning
Machine Learning Services for Enterprise
Historical data is a record. Machine learning makes it a forecast. Most organisations are good at reporting what happened. They are less good at knowing what is about to happen. Which customers are close to leaving. Where the next equipment failure is building. Whether this season's stock levels will hold. The answers are in the data. They are just not surfaced in time to act on. DTC Infotech builds machine learning systems that change that. Models trained on your data, connected to the tools your teams already use, designed to reach the person who needs to make the decision before the moment has passed.
Only 31% of AI initiatives reached full production in 2025, double the rate from 2024, but still a signal that most machine learning projects do not survive the journey from pilot to production. (ISG, 2025)
What DTC Infotech builds in machine learning
Forecasting
Models that read live signals-sales, supplier quotes, weather, promotions-so teams plan from what is actually happening now.
Customer retention
Read engagement signals across your customer base and flag at-risk accounts while there is still time to intervene.
Risk and anomaly detection
Continuously monitor data across transactions, sensors, and records to surface patterns before they become problems.
Recommendations
Consider current activity, availability, and pricing to surface what is relevant to the moment, not just a replay of history.
Customer scoring
Score customers based on actual behaviour so teams call the right accounts and marketing spends where it works.
Operational planning
Learn demand peaks and failure patterns so planning reflects how the business actually moves, not just last year's average.
25-35% - Forecast AccuracyAverage improvement versus previous planning methods
40% - Churn ReductionReduction for organisations with deployed scoring models
Where machine learning is being deployed
Retail and consumer goods
Capabilities
Model design
We choose the right method-classification, regression, time-series-based on the prediction needs and required accuracy.
Data preparation
We work with your teams to identify critical data, fill gaps, and build pipelines that keep it current and accurate.
Production deployment
We handle the infrastructure, integration, and governance that takes a model from prototype to daily operational use.
Explaining the output
We build visibility into a model's reasoning so people acting on the output can understand and trust its conclusions.
Maintaining accuracy
We set up continuous monitoring to detect accuracy drift and control the process for updating performance over time.
Deployments
Impact
Banking
Banking - Real-time Fraud Detection
Manual review of flagged transactions was causing delays and missing sophisticated fraud patterns.
Fraud losses reduced by 50%. False positives decreased by 25%.
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Manufacturing
Manufacturing - Predictive Maintenance
Equipment failures were causing unscheduled downtime and expensive emergency repairs.
Unscheduled downtime reduced by 40%. Maintenance costs lowered by 20%.
View full case studyHow we work
The gap is not the algorithm. It is everything that sits between a working model and a decision.
What next
Most operations are sitting on data that could tell them what is coming. We can help identify where that gap is largest.
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