Live Business ML (BML) Models

Pharma Process Cost Estimation

A Business ML model trained on a synthetic paracetamol production dataset to estimate process costs. This proof-of-concept shows how neural networks can outperform spreadsheets in accuracy, flexibility, and insight. Read more

CMOS Cost Estimation (Coming Soon)

A Business ML model trained to estimate multi-step cost components in CMOS semiconductor fabrication. This work highlights how neural networks can replace static formulas and improve margin clarity in high-variability operations. Read more

BML Using Neural Networks

Business ML converts operational inputs into predictive cost and margin intelligence. Neural networks learn non-linear relationships that fixed spreadsheets cannot capture, and deliver fast, repeatable estimates that improve decision making across planning, pricing, and investment.

These demonstrations show how cost models can move from static formulas to learnable systems. The goal is to make estimation more accurate, more adaptable, and easier to validate against outcomes.

When cost becomes predictive, organizations can act earlier, price smarter, and manage margin with greater confidence.

Note: Models shown here are proof-of-concepts. Production deployments can be customized to match existing costing structures, data availability, and business objectives.