Sustainability
We develop Artificial Intelligence
applied to sustainability
AI-driven sustainability
At Qaleon, we rely on Artificial Intelligence to analyze the key sustainability issues to be assessed, with special emphasis on ESG criteria. to be assessed, with special emphasis on ESG criteria. This involves considering not only direct and indirect emissions along the supply chain in carbon footprint, but also promoting fair and safe working conditions, as well as fostering transparency and fairness in all our operations.
Use Case 1
Transparency and traceability in the supply chain
We use AI to ensure full transparency and traceability in the supply chain, allowing us to track the origin, movement and status of products in real time, from raw materials to the final consumer.
Positive impacts:
- Reduction of errors and fraud through immutable records and intelligent data analysis.
- Regulatory compliance and sustainability improvement by identifying unethical or unsustainable suppliers.
- Building customer trust and loyalty by demonstrating transparency and quality
Positive results
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Reduced response times to incidents or product recalls.
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Increased customer satisfaction and trust through transparency
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Decreased risk of non-compliance with regulations
Use Case 2
Prediction of energy consumption
Precise forecasting of energy consumption using artificial intelligence to optimize management, reduce costs and improve efficiency in industries, buildings and cities.
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Reduction of energy consumption
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Reduction of operating costs
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CO₂ emissions reduction.
Positive impacts:
- Optimize energy use by automatically adjusting systems such as air conditioning and lighting, resulting in a significant reduction in operating energy costs.
- Reduces energy waste and associated CO₂ emissions.
- Facilitates data-driven decision making to adjust production and energy storage, avoiding demand peaks and cost overruns.
Use Case 3
Measuring and forecasting organizational carbon footprint with AI
We rely on AI to measure and predict the organizational carbon footprint to identify the main sources of emissions, establish effective reduction strategies and comply with sustainability standards and regulations: CSRD, GRI, EINF, TCFD.
Expected results with numbers:
- Reduction of GHG emissions between 15% and 30% in three years after the implementation of improvement measures.
- Reduction of up to 25% in energy costs associated with the identification and correction of inefficiencies.
- Improved ESG rating and facilitates obtaining footprint certification seals such as MITERD's "Calculo y Reduzco" after verification and reduction of the carbon footprint.
Positive impacts
It allows defining clear goals and improvement plans to reduce emissions and energy costs.
Improve operational efficiency by identifying critical areas and optimizing processes.
Reinforces transparency and reputation with clients, investors and regulatory agencies.
Use Case 4
Intelligent waste sorting
We automate and optimize waste sorting through intelligence and machine vision to maximize recycling, reduce the amount of waste sent to landfills and improve operational efficiency.
Positive impacts:
- Increases accuracy and speed in the separation of recyclable materials, increasing the purity of recovered materials.
- It reduces the amount of waste that ends up in landfills, favoring the circular economy and reducing environmental impact.
- Improves occupational safety by reducing human intervention in hazardous tasks.
- Optimizes routes and processes, reducing operating costs and emissions associated with transportation.
Positive results
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Reduction of operating costs
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Increased recycling rates
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