Machine Learning - Predictive Models That Solve Real Problems | Detroit Computing
Machine Learning

Forecast your futurewith predictive models that work

We build machine learning systems that predict equipment failures, optimize operations, and automate decisions - integrated into your existing workflows.

Custom ML Models

Your data, your problems

Production Ready

Built to scale

ROI Focused

Measurable business impact

Get Started

ML problems we solve

Machine learning that drives business decisions

Predictive Maintenance

  • Predict equipment failures 2-3 weeks in advance
  • Reduce unplanned downtime by 60-80%
  • Optimize maintenance schedules and inventory
  • Works with existing sensor data

Demand Forecasting

  • Predict sales and inventory needs accurately
  • Reduce stockouts and overstock by 40%
  • Seasonal and trend pattern recognition
  • Integrates with existing ERP systems

Process Optimization

  • Optimize production parameters in real-time
  • Improve quality metrics and reduce waste
  • Automated anomaly detection and alerts
  • Learn from your best operators

Featured Case Study

ML-powered marketing optimization saving dealerships hundreds of thousands

Automotive

Marketing Measurement Company - Smart Budget Allocation

The Challenge

60% of marketing dollars spent are found to be ineffective or wasted. Car dealerships are spending over $500K annually on marketing and need smarter ways to budget across multiple channels without clear visibility into what drives real sales.

Our Solution

Engineered a SaaS tool that leverages state-of-the-art modeling to predict which marketing platforms drive real leads, visits, and sales. The system combines marketing mix modeling with reinforcement learning to optimize budget allocation in real-time.

Results Achieved

35%

Average savings

8 months

Delivery time

What-if

Analysis capability

Real-time

Budget optimization

Technologies Used

MMM
Machine Learning
Reinforcement Learning
Anomaly Detection
Forecasting
SaaS Platform

Our ML development process

From data exploration to production deployment

1

Data Assessment

Evaluate your data quality, identify patterns, and validate ML viability.

2

Model Development

Build and train custom models using your historical data and domain expertise.

3

Validation & Testing

Rigorous testing with real-world scenarios and performance validation.

4

Production Integration

Deploy models with monitoring, retraining, and integration to existing systems.

Let's talk about your project

Whether you have a clear vision or just a problem that needs solving, we'd love to hear about it.

Get in touch

Send us an email and we'll get back to you within 24 hours. No sales pitch, just an honest conversation.