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carbon reporting

“Automating High-Fidelity Carbon Reporting Extraction”

Actions Used:

🧠

Logical Reasoning

Analysis
🔗

Graph Interpretation

Analysis
🗺️

Document Navigation

Productivity
🤔

Self-Reflection

Analysis
🏗️

Structure Analysis

Analysis

Model Type:

OpenAI/Mistral for Multi-Agent Document Navigation and Review, Mistral/Llama for Graph Interpretation.

Problem

A leading consultancy in the carbon reporting space aims to estimate Scope 3 emissions for large organisations by extracting carbon emissions data from public sources — primarily company and sustainability reports.

These reports are often extensive, sometimes exceeding 400 pages, and contain a wide variety of formats, including text, tables, and graphical elements. Manually reviewing them is labour-intensive and costly, requiring analysts to spend significant time scanning and extracting relevant emissions figures across thousands of pages.

Simple Solution (Generic)

Feed the entire report into a large language model and prompt it to extract the necessary emissions data.

This approach is unreliable. General-purpose LLMs hallucinate figures when data isn’t explicitly available, struggle to interpret visual elements such as colour-coded graphs (e.g., Scope 1 in blue, Scope 2 in green), and cannot reliably process documents of this length and complexity. As a result, output is inconsistent, incomplete, and unfit for high-stakes analysis.

Tromero's Solution

Targeted. Structured. Scalable.

Tromero’s pipeline is designed specifically for large, complex, and heterogeneous corporate documents — combining symbolic reasoning, layout-aware navigation, and visual interpretation.
1. Multi-Agent Document Navigation
Intelligent traversal of large, unstructured documents
Tromero replicates expert document review by deploying agents that navigate reports based on known section headings, logical structure, and semantic cues. Instead of reading linearly, agents jump directly to sections such as executive summaries, sustainability disclosures, and visual appendices.
Rather than process 400 pages in sequence, Tromero identifies key content zones — increasing speed and precision.
2. Logical Reasoning for Structured Extraction
Verified, traceable, non-hallucinated data points
Data is not extracted blindly. Tromero uses symbolic reasoning to build structured emissions profiles incrementally, validating each figure against predefined templates and expected document logic. This ensures only explicitly sourced and internally consistent data is retained.
If a figure cannot be traced to a defined section, it is excluded — eliminating hallucinations and assumptions.
3. Graph Interpretation Modules
Visual parsing of complex, non-textual data
Tromero includes purpose-built interpreters for graphical data. These modules decode emissions charts — recognising colour-coding conventions and visual hierarchies — to extract values that would otherwise be inaccessible to standard models.
Tromero includes purpose-built interpreters for graphical data. These modules decode emissions charts — recognising colour-coding conventions and visual hierarchies — to extract values that would otherwise be inaccessible to standard models.

Outcome

The system operates at scale across thousands of documents, delivering high-fidelity emissions data extraction within strict reporting timelines.