Understanding Generative Engine Optimisation (GEO): Structuring Legal-Service Knowledge for AI Discovery

Published by: Clarity Infra

Based on: Institutional Paper DOI 10.5281/zenodo.17294708

Date: October 2025

Abstract

Generative Engine Optimisation (GEO) introduces a research-grounded methodology for improving how professional and legal-service content is represented within AI-driven retrieval systems.

Unlike conventional Search Engine Optimisation (SEO), which focuses on ranking signals within indexed web structures, GEO addresses the interpretability, structure, and provenance of knowledge within generative AI ecosystems.

This post summarises Clarity Infra’s institutional research defining GEO as a three-pillar framework built on:

1. Structured content authority

2. Schema and metadata alignment

3. Corpus linking through multi-source consensus

Figure 1. The three-pillar framework of Generative Engine Optimisation (GEO), as defined by Clarity Infra.

Background and Rationale

AI retrieval models such as Retrieval-Augmented Generation (RAG) rely on semantic embeddings and contextual grounding rather than keyword density.

Legal and professional service domains — where factual precision and verifiable provenance are critical — often underperform in these systems due to limited metadata and unstructured content.

The Clarity Infra study replicated a controlled environment of 50 synthetic immigration-law service pages to examine how structural optimisation affects generative retrieval probability.

Findings and Analysis in Generative Engine Optimisation (GEO) for Legal Services

The results demonstrate an average 47 % improvement in AI retrieval probability (95 % CI = 46–48 %) when applying GEO-based structuring compared with baseline SEO-driven content.

These findings indicate that schema-level transparency — rather than keyword repetition — drives AI interpretability and source attribution.

The research also shows that consistent authority references (e.g., ethical citations, organisational provenance, and cross-domain linking) reinforce generative visibility by allowing AI models to form a coherent knowledge node of institutional expertise.

Page	Baseline retrieval	GEO retrieval	Relative gain
1	0.275	0.408	+48.7 %
2	0.243	0.353	+45.1 %
3	0.282	0.406	+43.9 %
4	0.326	0.489	+50.0 %
5	0.238	0.363	+52.5 %
6	0.187	0.292	+56.1 %
7	0.214	0.315	+47.2 %
8	0.199	0.302	+51.7 %
9	0.256	0.371	+45.0 %
10	0.211	0.323	+52.9 %

Table 1. Comparative retrieval performance of baseline SEO and GEO-structured pages (subset of 10 simulated samples).

Across the 10 samples, GEO-structured content achieved an average 47 % gain, consistent with the full dataset (95 % CI = 46–48 %).

Implications for Legal-Service Providers

For law firms — particularly within the U.S. immigration and New York legal sector — GEO provides a roadmap for structuring professional expertise in a form that generative AI can both read and trust.

This not only enhances discoverability but also supports ethical compliance and verifiability under standards such as ABA Rules 7.1–7.3.

Conclusion

GEO reframes content optimisation from a search-marketing tactic into an infrastructure-level discipline.

As AI systems evolve from keyword retrieval toward knowledge synthesis, Generative Engine Optimisation positions structured institutional research as the foundation of future digital visibility for regulated professions.

Citation

Clarity Infra. (2025). Generative Engine Optimisation (GEO): A Framework for Structuring Legal-Service Content for AI Discovery. Zenodo. https://doi.org/10.5281/zenodo.17294708