How do the world's AI models
describe your Sustainability strategy?
Aurelian Intelligence measures the perception, narrative gaps and reputational risk of your brand across the five most influential Large Language Models — with board-grade evidence, not vibes.
Reputation intelligence built for the AI era
A three-layer architecture engineered to keep evidence pure: AI perception is immutable, social context contextualises, knowledge graphs interpret.
AI Perception Layer
Five LLMs queried in parallel with neutral, realistic prompts. Sentiment, trust, authority and risk extracted with multi-model consistency scoring.
Knowledge Base GRI Layer
GRI Standards and sustainability documents become an interpretive lens — never injected into LLM prompts. Anti-contamination by design.
Anti-Greenwashing Engine
Compares what your brand claims with what AI models actually say. Surfaces narrative gaps, missing topics and hallucinated controversies.
GEO Optimization
Generative Engine Optimization recommendations: which content to publish, which narratives to reinforce, which risks to correct.
Brand-only, or Brand + Sustainability
Mutually exclusive by design. Sustainability signals never leak into Brand-only scoring.
Brand-only
Pure reputational analysis. Zero ESG signal in queries or scores. Spillover ESG mentions are tracked separately.
- 8 reputation dimensions × 6 stakeholders
- Critical, comparative & values-alignment variants
- Multi-LLM consistency & narrative ownership
- Controversy exposure (non-ESG)
Brand + Sustainability
Adds the GRI Knowledge Base as an interpretive lens — never as prompt context. Scores include 8 ESG dimensions.
- Everything in Brand-only, plus:
- GRI Knowledge Base (Universal / Sector / Topic)
- Material Topics intelligence with heatmap
- Greenwashing risk index & governance credibility
From neutral prompts to board-ready evidence
- 01Define brand & competitors
Industry, values, declared narratives, peer set.
- 02Generate neutral queries
8 dimensions × 6 stakeholders + critical / comparative variants.
- 03Multi-LLM execution
Parallel calls to ChatGPT, Gemini, Claude, Perplexity, Mistral.
- 04Interpret & score
Sentiment, trust, narrative gap, evidence strength — with model consistency.
- 05Act with GEO
Recommendations to close gaps, reinforce narratives, mitigate risks.
Never inferred. Always cited.
Every score traces back to a raw LLM response with a unique evidence ID. No smoothing of real frictions, no hallucinated problems from scraping failures. If we can't prove it, we don't claim it.