Kerra
Kerra provides a computational material discovery engine that predicts keratin-based ingredient behavior before physical experimentation, screening combinations against commercial viability, toxicity, and scalability. The platform leverages a specific keratin monomer (A0ABC9XWU5, 98 residues) with machine-learning models to serve applications across cosmetics, textiles, and industrial coatings. Kerra works with a supplier network across three countries to ensure whole keratin feedstock continuity at industrial scale.
- New Materials
- Chemical Technology
- Artificial Intelligence
- Software Only
- Clean Technology
Funding
Founders
Founder details are not available yet.
Product
Problem
Traditional material and ingredient development relies on costly, time-consuming trial-and-error experimentation, making it difficult to assess commercial viability, toxicity, and scalability before committing resources. The beauty, materials, and industrial sectors lack predictive tools to screen ingredient combinations against real-world constraints like reaction thermodynamics and polymer network physics.
Solution
Kerra provides a computational material discovery engine that predicts material behavior before the first experiment, weighing commercial viability, toxicity, and scalability of each ingredient. The platform screens thousands of combinations against commercial availability, reaction thermodynamics, and polymer network physics. Kerra works with a supplier network across three countries to ensure feedstock continuity for whole keratin at industrial scale, making abundant, renewable, and low-cost feedstock accessible for next-generation ingredients. The platform enables green chemistry principles with tunable results and lean manufacturing processes.
Target Audience
Primary customers are product developers and formulators in cosmetics, personal care, textiles, industrial coatings, packaging, and biotech sectors seeking sustainable keratin-based ingredients and predictive material development tools.
Features
- Computational platform predicting material behavior before physical experimentation
- Screening of thousands of ingredient combinations against commercial availability, reaction thermodynamics, and polymer network physics
- Keratin monomer identity tracking (A0ABC9XWU5, 98 residues) with ML materials discovery models
- Applications across 20+ categories including hydrogels, hair repair, super-absorbent gels, makeup, textiles, fibers, conditioners, non-wovens, film formers, composites, surfactants, carbomer replacements, serums, adhesives, binders, industrial coatings, resins, cosmetics, skin care, thickening agents, water repellents, packaging, and biotech
- Supplier network across three countries ensuring feedstock continuity at industrial scale