Quastify offers a quantum‑mechanics‑based AI platform that creates digital twins of chemicals and materials, enabling faster, more cost‑effective discovery for the chemical, pharmaceutical, and materials sectors. By learning from each customer interaction, the system can reduce R&D time and expense by 10‑100×, helping companies bring new drugs, semiconductors, batteries, and other advanced materials to market more quickly.
Funding
Funding not disclosed
Founders
Product
Problem
Discovering new drugs, semiconductors, batteries, solar materials, and advanced ceramics requires extensive experimental cycles, making R&D slow and financially burdensome. The reliance on trial‑and‑error limits the speed at which companies can bring innovative products to market.
Solution
Quastify offers AI‑driven digital twins that model chemical and material behavior using quantum‑mechanics‑based machine learning. The platform continuously refines its predictions from each client interaction, delivering increasingly accurate suggestions for new molecules and materials. By embedding fundamental physical principles, the models provide transferable insights that reduce the number of required experiments. This approach can cut R&D timelines and costs by an order of magnitude to two orders of magnitude, enabling faster product development. The digital twin integrates with existing discovery workflows, allowing teams to evaluate candidates virtually before committing laboratory resources.
Target Audience
Primary customers are R&D teams in pharmaceutical, chemical, and advanced materials companies that conduct drug discovery, battery development, solar panel research, or ceramic engineering.
Features
- Quantum‑mechanics‑grounded AI models that predict molecular and material properties with high fidelity
- Continuous learning loop that updates the digital twin from every user query and experimental result
- Scalable cloud infrastructure supporting high‑throughput virtual screening of large chemical spaces
- API and workflow integrations that embed the digital twin into existing R&D pipelines
- Cost and time reduction estimates of 10‑100× compared to traditional experimental approaches