Artificial Genius provides two AI‑driven platforms for high‑risk design challenges: an Engineering Design OS that uses causal modeling, uncertainty quantification, and ethical constraints to guide engineers through complex, data‑sparse systems, and an Explainable Protein Designer that generates intentional protein sequences with transparent, mechanistic predictions. Both tools deliver adaptive optimization, risk assessment, and explainable outputs via a unified web interface, helping engineering and biotech firms reduce experimental costs and improve decision‑making in uncertain environments.
Funding
Funding not disclosed
Founders
Product
Problem
Designing complex engineering systems and novel proteins involves high uncertainty, multiple failure modes, costly data acquisition, and ethical considerations, making traditional design methods inefficient and risky.
Solution
Artificial Genius offers two AI-driven platforms to address these challenges. The Engineering Design OS provides an adaptive, risk-aware design environment that integrates causal modeling, uncertainty quantification, and ethical constraints to guide engineers through complex, data-sparse problems. The Explainable Protein Designer leverages mechanistic understanding of protein geometry, chemistry, and solvation to generate intentional protein sequences with transparent, interpretable predictions. Both platforms use advanced machine‑learning techniques combined with domain‑specific causal models, enabling rapid iteration, reduced experimental costs, and more reliable outcomes. Users interact via a unified interface that presents design alternatives, risk assessments, and explanatory insights, supporting informed decision‑making in dynamic and novel environments.
Target Audience
Primary customers are engineering firms, aerospace and automotive manufacturers, and biotech or pharmaceutical companies engaged in high‑risk system design and protein engineering.
Features
- Causal, physics‑based modeling core that captures underlying mechanisms in engineering systems and protein biology
- Integrated uncertainty quantification and risk assessment to evaluate multiple failure modes
- Explainable AI outputs that provide transparent rationale for design choices and predictions
- Adaptive optimization loops that iteratively refine designs with minimal additional data
- Ethical constraint module allowing users to embed regulatory and safety considerations directly into the design process
- Unified web‑based interface for collaborative design, scenario exploration, and result visualization