Methodology
Built on structured methodology,
not GEO theater.
Hordus combines entity intelligence, structured data logic, citation analysis, behavioral modeling, and model-respecting methods to improve how brands are understood in AI systems.
Six pillars
What the methodology covers.
Entity Intelligence
Understanding how AI models represent, disambiguate, and associate entities (brands, products, concepts) in their internal knowledge structures.
Structured Data Logic
Ensuring pages use schema markup, structured FAQs, and clear content hierarchies so models can parse, classify, and cite them reliably.
Citation Analysis
Mapping which domains, page types, and content formats AI models trust and cite most frequently when generating answers.
Behavioral Modeling
Using real user journey data — prompts, clicks, comparisons, and purchase signals — to ground strategy in how people actually research and decide.
Model-Respecting Methods
No adversarial tricks, no dark patterns, no exploitation of model vulnerabilities. Improvements are grounded in making content genuinely more useful and trustworthy.
Continuous Evaluation
Running evaluation packs — structured sets of prompts and expected facts — to test and track how AI answers change over time in response to interventions.
Data foundation
How our data works.
Hordus is built on real user journeys — consented, GDPR/CCPA-compliant panels with clickstream and purchase signals across markets. When available, we enrich journeys with demographics and location so your strategy reflects how people actually research and decide.
The platform does not rely on synthetic prompts or simulated browsing. Every signal comes from real behavior, giving you ground truth that drives confident decision-making.
Real user journeys
GDPR / CCPA compliant
Clickstream signals
Purchase signals
Demographic enrichment
No synthetic data
Get started
See the methodology
in action.
Book a demo and we'll walk through your brand's current AI answer landscape and what we'd do about it.