Applying Generative Engine Optimisation (GEO) for O-1A Immigration Law Firms in New York: Structuring Expertise for AI Search Systems

Published by: Clarity Infra

Based on: Institutional Paper DOI 10.5281/zenodo.17296236

Date: October 2025

Abstract

This institutional study applies Generative Engine Optimisation (GEO) to O-1A immigration law firms in New York, expanding upon Clarity Infra’s 2025 framework on AI search visibility for professional services.

The research examines how structured expertise and schema-aligned metadata improve retrievability in generative-AI search systems such as ChatGPT, Gemini, and Perplexity. By applying the GEO framework, the study demonstrates that schema transparency and authority linking substantially enhance how generative models interpret and surface legal-service knowledge.

Conceptual Framework

O-1A practice revolves around demonstrating “extraordinary ability” through verifiable achievements—awards, publications, and media citations.

These evidentiary structures map directly to the three pillars of GEO:

1. Structured Content & Authority — encoding credentials and expertise as verifiable entities;

2. Schema & Metadata Optimisation — tagging recognitions and citations for semantic retrieval;

3. Corpus Linking & Multi-Source Consensus — interlinking firm data with federal and third-party sources to reinforce credibility.

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

Findings and Analysis in Generative Engine Optimisation (GEO) for O-1A Immigration Law Firms

Evaluation was conducted across three embedding models widely used in AI retrieval systems. Each processed a corpus of synthetic O-1A immigration-law pages under both baseline SEO and GEO-structured formats. Performance was measured by Top-3 semantic retrieval accuracy.

Embedding model	Baseline Top‑3 accuracy	GEO Top‑3 accuracy	Improvement (abs.)	95 % CI (bootstrapped)	p‑value
MiniLM‑L6‑v2	0.41	0.61	+0.20 (49 %)	±0.05	0.001
E5‑base‑v2	0.46	0.68	+0.22 (48 %)	±0.04	0.0005
OpenAI text‑embedding‑3‑small	0.50	0.72	+0.22 (44 %)	±0.05	0.0003

Table 1. Embedding-level evaluation comparing baseline SEO corpora and GEO-structured O-1A datasets (N = 50 pages per model).

Across models, GEO yielded ≈ 0.21 absolute gain (≈ 45–50 % relative) in Top-3 retrieval accuracy (p < 0.001), confirming that schema-aligned structuring improves embedding interpretability and cross-model consistency.

Ethical and Professional Implications

Given the regulatory nature of legal communications, the study aligns with ABA Rules 7.1–7.3 to ensure that GEO enhances verifiability without marketing inflation.

Structured metadata enables AI systems to reproduce facts accurately while maintaining ethical and professional integrity in legal representation.

Conclusion

Applying GEO to O-1A immigration law firms demonstrates how professional expertise can be encoded into machine-readable authority without compromising ethics.

Clarity Infra’s research shows that structured knowledge not only improves retrieval metrics but also builds institutional trust within AI search ecosystems.

Citation

Clarity Infra. (2025). Applying Generative Engine Optimisation (GEO) to O-1A Immigration Law Firms in New York: Structuring Expertise for AI Discovery. Zenodo. https://doi.org/10.5281/zenodo.17296236