Artificial Intelligence Management Systems
ISO 42001 is the international standard for implementing, maintaining, and improving safe and ethical AI systems. Here's how we apply it to each sector.
What is ISO/IEC 42001?
ISO/IEC 42001 mandates that AI systems must be explainable. Standard deep learning models are often black boxes. They provide answers, but cannot explain why. In material science, the greatest risk is inventing impossible physics that lead to a wasted manufacturing run, a compromised safety test, or a delayed product launch. We prevent this with the architecture of our Physics-Informed Neural Networks.
Hard-Coded Equations
Our models don't just find patterns in data, they solve known equations (Nernst-Planck, Mechanics). When our model predicts a dendrite formation, it provides the underlying physical variables that caused it such as stress gradients, ion concentration, and potential drop. This allows your engineers to verify the Why behind every prediction.
Physics-Constrained Loss Functions
Unlike a standard Large Language Model that can say anything, our PINNs are mathematically penalized if they violate the laws of thermodynamics or conservation of mass. If the model predicts a scenario that is physically impossible (creating energy from nothing), the system rejects it during the training phase. We do not allow the AI to guess outside the boundaries of reality.
Managing Model Drift
Batteries are complex, and manufacturing conditions change. A model trained on Batch 1 might fail on Batch 10 if the pressure settings drift. We apply the ISO 42001 requirement for Continuous Monitoring:
- Drift Detection: We monitor the statistical distribution of your input data. If your factory sensors start reporting temperatures outside the range our model was trained on, our system flags the anomaly immediately rather than making a low-confidence prediction.
- Version Control: We maintain strict versioning of all physics kernels. If we update the underlying equations, we can trace exactly which version of the model was used to validate which batch of batteries.
What is ISO/IEC 42001?
ISO/IEC 42001 mandates that AI systems must be explainable. Standard deep learning models are often black boxes. They provide answers, but cannot explain why. In chemistry, the greatest risk is creating molecules that seem plausible but that are ultimately unsynthesizable. We prevent this with the architecture of our Graph Neural Networks.
Constrained Architecture
Our GNNs are architecturally constrained to a discrete action space of verified chemical reactions (e.g., Amide Coupling, Click Chemistry). It is mathematically impossible for our AI to propose a linker that violates the rules of valence or chemical logic. We treat synthesizability as a hard constraint that cannot be violated.
Explainability
Our system provides full traceability. For every linker proposed, we output the specific Enamine Catalog IDs of the starting fragments and the reaction sequence used to join them. We also accompany every prediction with the raw thermodynamic scores from our Physics Oracle (Vina/Rosetta), providing a clear, auditable rationale for why a specific linker was given a high score based on energy.
Data Quality
Our agents interact exclusively with the Enamine REAL database and high-fidelity crystal structures (PDB). This strict data governance ensures that the model's worldview is limited to commercially available, high-purity chemistry.
Human-in-the-Loop
Our technology is designed to present options, not final decisions. The AI generates a ranked list of high-probability candidates, but the final selection for synthesis remains in the hands of your medicinal chemists.
What is ISO/IEC 42001?
ISO/IEC 42001 mandates that AI systems must be explainable. Standard deep learning models are often black boxes. They provide answers, but cannot explain why. In banking, the greatest risk is liability. We handle this with the architecture of our Graph Neural Networks.
Explainable AI
Every risk score generated by our GNN is accompanied by readable Code and a visual map of the suspicious network connections. We don't just tell you a transaction is risky, we show you the specific star topology or mule chain that triggered the alert.
Graph Neutrality
By design, our GNN architecture focuses on network structures, such as the relationships between devices and accounts, which are inherently more objective indicators of fraud than traditional demographic profiling.
Continuous Monitoring
We monitoring the GNN constantly to detect Data Drift. If the patterns of normal banking behavior shift (e.g., during a holiday season), our system alerts our data scientists immediately, preventing false positives from spiking. Our system also learns from your analysts. When a human reviewer confirms or rejects a fraud alert, that feedback is securely fed back into the system, allowing the GNN to adapt to new fraudster tactics without manual reprogramming.
Human-in-the-Loop
We view AI as a precision tool for your investigators, not a replacement for human judgment. You define the boundaries. You can set the system to Auto-Block only on extreme confidence scores (e.g., >99% probability of a known Mule Ring), while routing complex, gray area cases to human experts for review.