Real-world case studies from AI SEO consulting engagements in 2026 — showing what's achievable, what worked, and what the measurement framework looked like. These examples illustrate the types of results possible from professional AI visibility optimisation.
Case Study: UK FinTech Brand
Challenge: Major UK neobank appearing in only 18% of relevant ChatGPT queries despite strong brand recognition. Diagnosis: Poor entity authority — inconsistent sameAs, no FAQPage schema, llms.txt missing. Solution: Complete schema rebuild, llms.txt implementation, expert author attribution programme. Results at 90 days: 18% → 61% inclusion rate in ChatGPT. 22% → 48% in Perplexity. AI-referred traffic +340%.
Case Study: B2B SaaS Platform
Challenge: Enterprise HR software brand invisible in Perplexity and Copilot for 'best HR software' queries. Diagnosis: Competitor co-citation analysis showed competitors were being mentioned in product comparison pages — our client was absent. Solution: Information gain content strategy targeting comparison queries, competitor gap analysis, schema optimisation. Results at 60 days: Appeared in 71% of target comparison queries across platforms. Pipeline from AI referrals increased 28%.
Case Study: Professional Services Firm
Challenge: Management consultancy losing ground to newer competitors in AI recommendations. Diagnosis: Thin author entity signals — no named experts with verifiable credentials, generic content. Solution: Expert entity building for 3 senior consultants, LinkedIn optimisation, published research programme. Results: 3.8x increase in citations including named expert attribution. 45% of citations now mention a named partner by name.
Key Lessons Across Engagements
1. Entity authority is always the first fix — before content, before links. 2. Schema completeness has the fastest ROI (2-3 weeks to measurable improvement). 3. Platform variation is real — a brand can dominate ChatGPT but be absent in Perplexity. Always measure all platforms. 4. AI visibility drops often happen silently — continuous monitoring is essential.
How to Interpret AI SEO Case Studies
Look for: baseline and result metrics (not just results), timeline to results, specific tactics used (not vague 'optimisation'), platform-specific data, and connection to business outcomes (pipeline, not just traffic). Be sceptical of case studies showing only traffic metrics without AI visibility data.
Expert insight: By Yuliya Halavachova, Founder & Principal Data Scientist at UltraScout AI — Principal Data Scientist with 16+ years building enterprise AI solutions with large language models (LLMs).
Frequently Asked Questions
How long do these results take?
Technical changes (schema, llms.txt) show results in 2-3 weeks. Content strategy impacts appear at 8-12 weeks. Expert entity building compounds over 3-6 months. Full programme results are typically measured at 90 days and 6 months.
Are these results typical?
Results vary by starting baseline, industry, budget, and execution quality. Brands starting from very low visibility see the largest percentage gains. Brands already at 40-50% inclusion rates see smaller percentage improvements but often larger absolute business impact.
Can I see UltraScout AI's own case studies?
Yes — UltraScout AI has published AI visibility reports for HSBC, Monzo, Revolut, British Airways, Octopus Energy, and many more UK brands. Visit ultrascout.ai/aeo-case-studies for the full library.