Turn Data into Dollars – Improve Profit by 2-12%

Custom Machine Learning solutions that combine Process ML (PML) and Business ML (BML) to unlock hidden value in your operations.

PML+BML: A Proven Path to Profit

Organizations generate vast amounts of data from sensors, spreadsheets, and day-to-day operations. Yet much of this information remains underutilized. At MLPowersAI, we turn that untapped data into measurable business value using two complementary branches of applied AI: Process ML (PML) and Business ML (BML).

Process ML (PML): Predict, Optimize, and Improve Operations

Process ML applies machine learning to real-time and historical process data to predict outcomes, reduce variability, and optimize performance. It enables companies to go beyond reactive control and move toward predictive operations that improve yield, throughput, and reliability.

We build domain-specific models that understand your processes — chemical plants, pharmaceutical operations, semiconductor fabs, food technologies, energy systems, and more. Our live demo models on plasma etch rate prediction and CSTR prediction show how PML can accurately forecast process behavior. Once a reliable ML model is established, we enable real-time optimization through predictive control — as demonstrated in our IEEE preprint on plasma etch optimization.

Whether you’re aiming to produce more, reduce scrap, stabilize cycle times, or anticipate bottlenecks, PML turns operational data into a strategic advantage — one that directly impacts profitability.

Business ML (BML): Forecast Costs, Improve Margin Accuracy

Business ML applies machine learning to business operations and financial models — replacing spreadsheet assumptions with intelligent predictions grounded in real-world data. BML helps uncover hidden drivers of cost and profitability that traditional methods often miss.

We develop ML models that improve forecasting across multiple domains: process cost estimation, raw material pricing, indirect cost modeling, and capital budgeting. Our Paracetamol process cost demo and CMOS semiconductor cost case study illustrate how BML can replace static formulas with predictive, context-aware logic. These models enable finance and operations teams to make better decisions — with less bias and more clarity.

By modeling cost components the way a neural network sees them — through patterns, interactions, and residuals — BML delivers actionable insights. Businesses gain improved cost accuracy, enhanced pricing strategies, and greater confidence in scenario planning. For many operations, these improvements translate directly into 2–12% gains in profit margin.

Other Neural Network Models

Beyond Process and Business ML, we build custom neural network models for a range of real-world problems. These models are tailored to the unique structure and signals within each domain — from time series forecasting to classification and anomaly detection.

One example is our live stock price forecasting demo, which uses a lightweight neural network to predict next-day movements in high-profile stocks. The methodology behind this model is explained in our article, “AI/ML in Finance: How a Lightweight Neural Network Forecasts NVDA’s Next Stock Price Move”. These types of models show how neural networks can be used for financial forecasting, risk estimation, and pattern detection across sectors.

We also apply neural networks to healthcare, where early and accurate predictions can significantly impact outcomes. Our breast cancer prediction model illustrates a proof-of-concept binary classification system that analyzes medical features to predict the likelihood of malignancy. While not intended for clinical use, it demonstrates how machine learning can support diagnostic decisions by providing rapid, scalable insights for healthcare workflows.

We continue to explore new applications where custom ML can add strategic value — wherever data and decisions intersect.

Explore Our Work

Ready to see real examples in action? Start here:

🌐 Live Models: models.mlpowersai.com
📝 Technical Articles: mlpowersai.com/pulse