Carbon Footprint Analysis POC
Using AI and vector search to automate environmental impact analysis for construction materials.
Mapping Accuracy
95% accuracy in material mapping
Processing
Millions of order lines analyzed automatically
Analysis
Real-time carbon footprint calculations
Impact
Clear pathways for sustainability optimization
The Challenge
A construction company needed to measure and optimize their environmental impact across their supply chain. They had millions of order lines and materials to analyze, but mapping these to carbon footprint data manually was slow and error-prone.
They needed an automated way to understand and reduce their carbon footprint while keeping their operations efficient.
Our Solution
We built an AI system that automatically maps construction materials to public carbon footprint databases, considering both material composition and transportation factors.
Key Features
- • Automatic processing of order lines
- • Material mapping to carbon databases
- • Transportation emissions calculation
- • Actionable sustainability insights
Technical Architecture
- • Vector database for material matching
- • Machine learning for classification
- • Automated data enrichment pipeline
- • Real-time calculation engine
Implementation Process
The proof of concept included:
- 1. Material analysis using vector similarity search
- 2. Multi-stage verification with confidence scoring
- 3. Integration with carbon footprint databases
- 4. Transportation impact analysis
The solution gave the client clear visibility into their environmental impact, helping them make better decisions about material selection and supply chain optimization. This improved both cost efficiency and environmental impact.