Carbon Footprint Modeling

Integration with IoT sensors and ERP systems to provide real-time Scope 1, 2, and 3 emissions tracking with AI-driven gap filling for missing data points.

Climate Risk Stress Testing

Monte Carlo simulations applied to asset portfolios to predict financial impact under various IPCC RCP climate scenarios (1.5°C to 4°C).

Social & Human Capital AI

Utilizing NLP to analyze glassdoor reviews, labor union data, and supply chain audit reports to score diversity, equity, and human rights compliance.

Automated Scope 3 Mapping

Using Graph Neural Networks (GNNs), we map global supply chain tiers to identify hidden carbon hotspots and labor risks. Our AI resolves entity reconciliation issues across millions of suppliers to provide a unified risk view.

Governance Sentiment AI

Proprietary NLP models analyze board meeting transcripts, executive communications, and Glassdoor reviews to quantify “G” factors. We detect early signals of cultural drift or governance failure before they manifest in financial losses.

Climate VaR Forecasting

We leverage Monte Carlo simulations and Recurrent Neural Networks (RNNs) to calculate Climate Value-at-Risk (VaR). This allows CFOs to understand the financial impact of various warming scenarios on their specific asset portfolio.

Neural Decarbonization & Digital Twin Simulation

For heavy industries and utility providers, achieving Net Zero requires more than just offsets; it demands a fundamental re-engineering of the carbon intensity of every operational asset. Our solution utilizes Digital Twins combined with Reinforcement Learning (RL) to simulate millions of decarbonization pathways.

By ingesting real-time telemetry from industrial IoT sensors, the AI identifies non-linear relationships between fuel consumption, ambient conditions, and output efficiency. This enables the autonomous optimization of load balancing and energy dispatch, significantly reducing Scope 1 emissions while maintaining grid stability and peak-load performance.

Scope 3 Integrity via Satellite-Derived Computer Vision

Monitoring “Scope 3” emissions and human rights compliance across Tier-3 and Tier-4 suppliers has historically been a black box. Sabalynx deploys a multi-modal AI architecture that fuses satellite imagery (SAR and Multispectral) with on-the-ground social media sentiment and news analysis to verify supplier claims.

The Computer Vision (CV) models detect unauthorized deforestation, infrastructure changes, and unusual logistics patterns that indicate non-compliance or labor risks. This proactive approach allows CPOs to mitigate ESG risks before they manifest as regulatory fines or reputational damage, ensuring a truly defensible and transparent supply chain audit trail.

Geospatial AI for Physical & Transition Risk Stress Testing

Global banks and institutional investors face increasing pressure to disclose climate-related financial risks. Our Geospatial AI platform integrates historical climate data with future predictive climate models to stress-test real estate and infrastructure portfolios against physical risks like sea-level rise, heatwaves, and flooding.

Furthermore, the platform models “Transition Risks”—the financial impact of sudden policy changes or carbon taxes. By applying Bayesian networks, we calculate the Value-at-Risk (VaR) for diverse assets, providing investment committees with the quantitative rigor required for TCFD (Task Force on Climate-related Financial Disclosures) compliance and long-term capital allocation strategies.

Agentic AI for Cross-Border CSRD/SFDR Taxonomy Mapping

The regulatory landscape for ESG is fragmented and rapidly evolving across jurisdictions. Sabalynx utilizes Agentic AI systems—powered by Retrieval-Augmented Generation (RAG)—to autonomously ingest, interpret, and map corporate activities to global taxonomies like the EU’s CSRD, SFDR, and the ISSB’s international standards.

Unlike standard document search, our AI agents perform cross-document reasoning to identify data gaps in sustainability reports and automatically draft evidence-backed disclosures. This system ensures that large, multinational corporations maintain consistent reporting standards, significantly reducing the manual burden on ESG teams and ensuring auditable data provenance for every metric reported.

Predictive Circularity & Cradle-to-Gate LCA Optimization

Transitioning to a circular economy requires deep insight into the lifecycle of every product. We deploy AI-driven Life Cycle Assessment (LCA) tools that provide predictive insights into material valorization. By analyzing chemical compositions and historical failure rates, our models predict the optimal time for refurbishing versus recycling individual components.

This “Cradle-to-Gate” optimization engine integrates with PLM (Product Lifecycle Management) systems to suggest design modifications that lower carbon footprints before a product even reaches the manufacturing floor. The result is a significant increase in material recovery rates and a measurable reduction in the embodied carbon of finished goods.

Algorithmic Transparency & Privacy-Preserving DEI Analytics

Social responsibility hinges on fair treatment and equity within the workforce. Sabalynx deploys privacy-preserving AI frameworks, such as Differential Privacy and Federated Learning, to analyze Talent Lifecycle data for unconscious bias without compromising individual employee anonymity.

Our NLP models monitor internal communications and performance review cycles to detect systemic bias in promotions, compensation, and hiring pipelines. By providing CHROs with objective, data-driven DEI benchmarks, we move beyond subjective anecdotes into quantifiable social performance metrics that are defensible to boards, regulators, and activists alike.