Prompt a more sustainable world
Our Approach to Artificial Intelligence (AI)
AI Built for the Complexity of Sustainability
How does Rio stay relevant?
The answer lies in our purpose-built focus. While general-purpose AI models evolve, sustainability data and operational intelligence remain uniquely complex, fragmented, and regulated. That’s where Rio excels.
We’re not trying to build a one-size-fits-all model. We’re applying AI — responsibly, transparently, and effectively — to solve very specific challenges in sustainability measurement, environmental reporting, risk governance, and decision support.
Siemens uses neurosymbolic AI in industrial automation, combining sensor data (neural) with logical process modeling (symbolic).
Neurosymbolic AI
At the heart of Rio’s platform is neurosymbolic AI — a next-generation approach that fuses machine learning with symbolic reasoning. Unlike traditional AI that operates as a black box, neurosymbolic AI brings together data-driven learning and rule-based logic to generate outcomes that are not only intelligent, but explainable and verifiable.
- Combines statistical models with human-defined rules
- Supports auditability, traceability, and accountability
- Ideal for regulated, high-stakes sustainability environments
This hybrid model is especially powerful for sustainability reporting, where decisions must align with formal regulatory frameworks and taxonomies.
Enterprises that Trust Rio
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Integrate seamlessly with Rio
Data Engine & Integration
Rio’s platform connects directly to the systems that run your business — ERP, finance, supply chain, IoT — enabling real-time data ingestion without disruption.
- Automated data collection from operational systems and suppliers
- Standardised data mapping across multiple formats
- APIs for seamless integration with existing platforms
This foundation ensures Rio captures the full picture — not just top-level estimates.
Sustainability Knowledge Graphs
Rio’s AI is enriched with proprietary sustainability knowledge graphs — machine-readable representations of ESG frameworks, regulations, and sector-specific guidance.
This means:
- Data is automatically classified and contextualised
- Regulatory logic (e.g., ISSB, CSRD, SFDR) is embedded in the system
- Updates to frameworks are dynamically integrated into workflows
This capability enables rapid alignment with evolving global standards.