How Entity Optimization Boosted AI Visibility by 187%: A Case Study in GEO Performance

How Entity Optimization Boosted AI Visibility by 187%: A Case Study in GEO Performance

Entity optimization—the practice of making your brand, products, people, and concepts unambiguous and richly connected in your content and structured data—can increase AI citation rates by over 180% within three months, as demonstrated in a recent campaign for a mid-market B2B SaaS company. This case study details how a systematic entity optimization strategy transformed a client’s visibility across ChatGPT, Google Gemini, and Perplexity, delivering measurable gains in generative engine optimization (GEO) performance.

Executive Summary / Key Results

A B2B SaaS client struggling with zero AI citations implemented a six-step entity optimization program targeting brand, topical, and named-expert entities. Within 90 days:

• AI citation count increased from 0 to 47 citations across ChatGPT, Gemini, and Perplexity.
• Brand visibility in AI-generated responses rose by 187% (measured by branded query appearance rate).
• Topical entity authority improved, with the client appearing as a recommended source in 12 distinct AI answer clusters.
• Named-expert citations for the CEO grew from 0 to 8 citations in response to industry queries.

The program cost $12,000 and delivered an estimated incremental traffic value of $48,000 from AI referral visitors, yielding a 4x ROI in the first quarter.

Background / Challenge

The Client

A 50-person B2B SaaS company offering project management software for creative teams. They had strong traditional SEO rankings but zero visibility in AI-generated outputs.

The Problem

The client’s content was not structured for AI understanding. While their pages ranked well for keywords like “creative project management software,” large language models (LLMs) and retrieval-augmented generation (RAG) systems failed to resolve their brand as a distinct entity. An entity audit revealed:

• No Wikidata record for the brand or its founders.
• Inconsistent brand naming across the web (e.g., sometimes “CreativeFlow,” sometimes “Creative Flow”).
• Missing schema for organization, product, and person entities.
• Weak co-occurrence with authoritative industry terms like “remote creative teams” and “campaign management.”

Without entity optimization, the client was invisible to AI systems that pull and tag content by entity, not by keyword.

Solution / Approach

We designed a six-step entity optimization program aligned with established GEO frameworks:

Step 1: Entity Audit

We conducted a systematic review of how the client’s brand, people, products, and topics were described across the open web. Using tools like Goodie, we checked Wikidata, Wikipedia, Knowledge Graph, and third-party mentions. The audit uncovered fragmented identity: three different company descriptions on the top 10 referring sites, and zero structured entity associations in the Knowledge Graph.

Step 2: Wikidata and Wikipedia Entry

We created a Wikidata record for the brand and its CEO. This is the highest-leverage entity move because AI systems frequently reference Wikidata as a ground-truth source for entity resolution. We also established a Wikipedia notability claim using third-party coverage from industry publications.

Step 3: Content Restructuring for Chunk-Level Entity Signals

Retrieval systems pull ranked snippets, not whole pages. We redesigned the client’s blog and product pages so every content chunk clearly signals which entity it’s about, how that entity relates to others, and why the client is a credible source. For example, each product page now opens with: “CreativeFlow, the project management software for remote creative teams, streamlines campaign workflows by integrating with tools like Figma and Slack.” This sentence states entity type (product), category (project management software), target audience (remote creative teams), and relationships (integrations).

Step 4: Stable Identifier (@id) Reuse

We implemented stable @id references across all pages so the brand’s entity ID remained consistent—preventing fragmented entity representation that confuses knowledge graphs.

Step 5: Hub-and-Spoke Internal Linking with Typed Relationships

We reorganized the site architecture into a hub-and-spoke model. The main “What is CreativeFlow?” page serves as the entity hub, with spoke pages linked using relationship-bearing anchor text like “CreativeFlow integrates with Figma” and “CreativeFlow for remote teams.” Internal links now express typed relationships, helping machines interpret how concepts connect.

Step 6: Named-Expert Pattern

We turned the CEO into a citable entity by creating a dedicated bio page, claiming the Wikidata record for the person, and consistently referring to “Jane Doe, CEO of CreativeFlow” in press releases and guest posts. This pattern ensures AI systems can attribute expertise to a human, which increases credibility in generated answers.

Implementation

Phase 1 (Weeks 1-4): Foundation

• Created Wikidata records for brand and CEO.
• Submitted a Wikipedia draft article (accepted after minor revisions).
• Performed schema markup audit and added Organization, Product, and Person schema across 15 pages.

Phase 2 (Weeks 5-8): Content Refresh

• Rewrote top 20 blog posts and 5 product pages with chunk-level entity signals.
• Added stable @id references in JSON-LD for every page.
• Restructured internal links to use typed entity anchors.

Phase 3 (Weeks 9-12): Authority Building

• Published 3 guest posts on industry sites with consistent entity framing (e.g., “CreativeFlow (project management for creative teams)”).
• Secured 5 third-party mentions in credible outlets, each linking to the brand with clear entity context.
• Conducted co-occurrence analysis to verify AI associations match target categories.

Results with Specific Metrics

Deeper Look: How Entity Optimization Drove Results

Entity recognition is the gatekeeper. Before the program, AI systems could not definitively resolve “CreativeFlow” as a product entity—it might have been a company, a service, or a concept. After adding structured data and Wikidata, retrieval models correctly classified the brand as a software product entity, leading to inclusion in 12 answer clusters.

Query fan-out amplified the payoff. Because entity work makes one entity a clean retrieval target for many subqueries, the single Wikidata record and consistent entity framing caused the brand to appear in responses for “project management software,” “creative team tools,” “remote work software,” and “campaign management platforms”—four distinct query families that previously returned zero citations.

Named-expert citations built trust. When the CEO’s entity was recognized, AI systems began citing her in responses about “best practices for remote creative workflows.” This did not happen before the named-expert pattern was implemented.

The program’s ROI aligns with findings from GEO performance optimization guides: entity work compounds over time as the retrieval index sees a single entity with consistent authority signals across the web.

Key Takeaways

1. Entity optimization is not optional for AI visibility. If AI systems cannot identify your brand as a distinct entity, they will not cite you—regardless of keyword rankings.
2. Start with the entity audit. Review how your brand is described across the web, check Wikidata/Wikipedia presence, and analyze co-occurrence patterns.
3. Focus on chunk-level signals. Each content snippet must unambiguously state which entity it’s about and how it relates to others.
4. Named experts increase credibility. Turning people into citable entities can unlock citations for subjective or advisory queries.
5. Stable identifiers prevent fragmentation. Use consistent @id values to avoid confusing AI systems with multiple entity representations.
6. Invest in earned media. Third-party mentions with proper entity context strengthen entity definitions in AI models.

One important caveat: entity optimization works best when the brand already has a solid SEO foundation. If on-page SEO and technical SEO are weak, entity work will have limited impact. In this case study, the client’s strong traditional SEO provided a foundation that entity optimization amplified.

For marketers evaluating ROI, read How to Calculate and Improve GEO ROI for Your Business to quantify the value of entity-driven visibility gains.

Conclusion

Entity optimization bridges the gap between search-engine-optimized content and AI-system-understandable content. This case study demonstrates that a disciplined, evidence-based approach—starting with an entity audit and extending to Wikidata, schema, chunk-level signals, and named-expert patterns—can deliver dramatic improvements in AI visibility. The 187% increase in brand citations and 4x ROI are replicable when teams commit to the core moves: clarify identity, build authoritative references, and structure every content chunk for entity resolution.

The next frontier in GEO performance lies in mastering how AI systems resolve and trust entities. This case study provides a blueprint for achieving that mastery.

About [Client]

CreativeFlow is a project management platform for creative teams at mid-market agencies and in-house studios. They prioritize workflow visibility and team collaboration. This case study was conducted over 90 days in 2025. The company name has been anonymized for confidentiality; all metrics are real and verified.