Predictable Quality, Lower Cost — by Modeling Your Process as Triplet Chains

The Cost of Guesswork

• Trial-and-error R&D wastes materials and time
• Recipe changes cause unpredictable quality swings
• Process optimization relies on gut feel, not data
• Lab results don't scale to production

Why Now?

• AI can finally handle multi-stage, non-linear processes
• Modern sensors provide rich process data
• Regulatory pressure for transparent quality control
• Supply chain volatility demands adaptive formulations

Components

Raw materials with measurable physical/chemical properties

Example: Cement grade, aggregate size distribution, admixture chemistry

Recipe

Mix proportions and formulation ratios

Example: w/c ratio, binder content, fiber dosage

Regimes

Process conditions during production

Example: Curing temperature, mixing duration, pressure profile

Properties

Target outcomes you want to optimize

Example: Compressive strength, workability, cost per cubic meter

Cost Optimization

98th percentile optimality

Solutions outperformed 98% of historical recipes (Oviedo et al. 2024)

Prediction Accuracy

MAE < 5 MPa

Validated on 11,428 concrete mixes using CatBoost models

Realistic Timeline

3-9 months

PoC (3-4mo) → Advisory (6-9mo) → Production (12mo+)

Construction Materials

Concrete, cement, asphalt — optimize strength, durability, cost per m³

Plastics & Polymers

Injection molding, extrusion — balance mechanical properties with cycle time

Food & Beverage

Formulation, processing — control texture, taste, shelf life

Multi-Stage Processes

Any production with interconnected steps — model full triplet chains