--- title: "LLMO Case Studies: Brands Winning in AI Search (2026)" description: "Six verified Alice Labs LLMO case studies — +2,092% clicks, 83% cost reduction, 6,400-8,000 hours freed. 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Clicks grew from 141 to 3,091 (+2,092%) over 12 months across 8.77M total impressions. The full case is documented at /en/case/media-company-ai-seo-rewrite." } }, { "@type": "Question", "name": "How did the accounting firm reach 41 top-3 rankings in 3 months?", "acceptedAnswer": { "@type": "Answer", "text": "The engagement was a search authority rebuild combining LLMO foundations (entity clarity, Schema.org markup, citation-rich content) with topical authority work on advisory and regulatory queries. Over 3 months, top-3 rankings grew 0 → 41 and traffic +260% (2,500 → 9,000+ monthly visitors). Full case: /en/case/accounting-firm-ai-seo." } }, { "@type": "Question", "name": "What is the repeatable pattern across Alice Labs LLMO wins?", "acceptedAnswer": { "@type": "Answer", "text": "Every case applies the three Aggarwal et al. (2024) citation patterns — inline citations, named statistics, authoritative quotation — across sufficient content volume with editorial review before publish. The lift comes from scale and consistency, not from any single tactic. Production throughput is the binding constraint on most underperforming programmes." } }, { "@type": "Question", "name": "How long does an LLMO engagement take to show results?", "acceptedAnswer": { "@type": "Answer", "text": "Citation share moves first — usually within the first quarter for well-executed programmes. The accounting case showed top-3 ranking growth within 3 months. The media rewrite case compounded over 12 months. Production capacity is the rate-limiting variable: programmes with constrained editorial throughput take proportionally longer." } }, { "@type": "Question", "name": "Can these LLMO patterns work outside the Nordics?", "acceptedAnswer": { "@type": "Answer", "text": "Yes. The Aggarwal et al. (2024) tactics are platform-level, not market-level — they describe how generative engines select and rank citations across language and geography. Alice Labs cases are Nordic-rooted but the underlying mechanics (inline citations, named statistics, authoritative quotation, production discipline) transfer to any market." } }, { "@type": "Question", "name": "How do I verify the Alice Labs case study numbers?", "acceptedAnswer": { "@type": "Answer", "text": "Every case in this article links to a live /en/case/* engagement page on alicelabs.ai. The numbers used here are the same numbers we report in client-facing work and sales conversations. If you need additional verification for procurement or board purposes, request a reference call via /en/ai-search." } } ] }, { "@context": "https://schema.org", "@type": "Dataset", "name": "LLMO Case Studies: Brands Winning in AI Search", "description": "Six verified Alice Labs LLMO case studies — +2,092% clicks, 83% cost reduction, 6,400-8,000 hours freed. 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[Home](/en) [Insights](/en/insights) [AI Search & LLMO](/en/insights/ai-search) LLMO Case Studies: Brands Winning in AI Search AI Search & LLMO Data & Research Recent Last reviewed: 13 May 2026 · 118d ago # LLMO Case Studies: Brands Winning in AI Search ## TL;DR Quick Answer Cited by AI > LLMO case studies are documented client outcomes from large language model optimization work. Verified Alice Labs cases include a media company with +2,092% click increase, an accounting firm reaching 41 top-3 Google rankings in 3 months, and Ljusgårda saving 2.5M SEK/year through AI-augmented operations. Most LLMO case studies online are invented marketing. These six are verified Alice Labs client outcomes — same numbers we report to boards, with the same /en/case/\* references our sales team uses. LLMO case studies are documented client engagements showing measurable outcomes from large language model optimization — citation share, organic traffic, cost reduction, or operational throughput. A credible LLMO case study includes named programme scope, dated baselines, verified metrics, and a link to the underlying engagement page. The six Alice Labs cases in this article meet that bar. ![Linus Ingemarsson - Author at Alice Labs](/images/linus-ingemarsson.png) Written by [Linus Ingemarsson ](https://www.linkedin.com/in/linus-ingemarsson/) ![Eric Lundberg - Reviewer at Alice Labs](/images/eric-lundberg.png) Reviewed by [Eric Lundberg ](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Published May 6, 2026 · Updated May 13, 2026 11 min read +2,092% Click increase for an Alice Labs media client after AI-driven SEO rewriting of 178 articles [Alice Labs case study (verified)](https://alicelabs.ai/en/case/media-company-ai-seo-rewrite) 83% Cost reduction at Ljusgårda (Supernormal Greens) — 2.5M SEK/year saved through AI-augmented operations [Alice Labs case study (verified)](https://alicelabs.ai/en/case/ljusgarda-ai-agent) 6,400-8,000 Hours/year freed in an Alice Labs public-sector document automation engagement (60h to 3 min per document) [Alice Labs case study (verified)](https://alicelabs.ai/en/case/public-sector-document-automation) What you'll learn(6 points) - Why most LLMO case studies online are invented — and how to spot them - Six verified Alice Labs client cases with named outcomes and links - How a media company achieved +2,092% click growth with AI-driven rewriting - How an accounting firm went from 0 to 41 top-3 Google rankings in 3 months - How content-production discipline turned into 1M SEK first-year revenue - The repeatable pattern behind every Alice Labs LLMO engagement ## Key Takeaways - Most public LLMO case studies are fabricated. Demand named clients, dated baselines, and verifiable links before trusting any number. - An Alice Labs media client rewrote 178 articles using AI and grew clicks by +2,092% (141 → 3,091) on 8.77M impressions in 12 months. - An Alice Labs accounting firm engagement reached 41 top-3 Google rankings in 3 months and grew traffic +260% (2,500 → 9,000+ monthly visitors). - Ljusgårda (Supernormal Greens) cut operational costs 83% — 2.5M SEK/year saved, 6 to 1 FTE in 6 weeks. - A public sector document automation engagement freed 6,400-8,000 hours/year — 60h to 3 min per document. - Aggarwal et al. (2024) found citation-rich content lifts generative-engine visibility up to 40% (arXiv:2311.09735) — the published pattern behind every Alice Labs case. ### Contents 11 min left - [01 Why LLMO Case Studies Matter — and Why Most Online Are Fake ](#why-llmo-case-studies-matter) - [02 Case 1: Media Company AI SEO Rewrite — +2,092% Clicks in 12 Months ](#case-media-ai-seo-rewrite) - [03 Case 2: Accounting Firm AI SEO — 0 to 41 Top-3 Rankings in 3 Months ](#case-accounting-firm-seo) - [04 Case 3: Media Company Content Production — 1M SEK First-Year Revenue ](#case-media-content-production) - [05 The Pattern: Aggarwal's Citation Tactics, Applied at Scale ](#the-pattern) - [06 How Alice Labs Structures LLMO Engagements ](#alice-labs-engagement-process) Part of [AI Search Optimization: The Complete Guide for 2026](/en/insights/ai-search-optimization-guide) 01 / 06 Chapter ## Why LLMO Case Studies Matter — and Why Most Online Are Fake Most public LLMO case studies are invented. They cite anonymous 'Fortune 500 SaaS' wins with round-number outcomes and no verifiable link. Real LLMO case studies name the client (or programme type), publish dated baselines, and link to the underlying engagement page. Search "LLMO case study" in May 2026 and you will find dozens of vendor blog posts citing anonymous wins. "A Fortune 500 SaaS grew citations 5x." "A B2B brand 10x-ed its AI traffic." Almost none of those numbers can be verified. The clients are unnamed. The baselines are undefined. The methodology is absent. The numbers themselves are usually round — a marketing tell, not a measurement one. A credible LLMO case study has four things. Each is checkable before you trust a number. - **Named programme scope.** The client (or programme type), the work performed, and the time window. - **Dated baselines.** Where the metric started and when. "+2,092%" without a baseline is meaningless. - **Verifiable outcomes.** Specific numbers, not round ones. "+2,092%" is auditable; "+10x" is marketing. - **A link to the engagement page.** A live URL that documents the case beyond the marketing claim. The six Alice Labs cases below each meet that bar. They are the same engagements we reference in sales conversations and board reviews. The metrics are the audited numbers, not the best-case version. Spot the fake case study If a case study uses a round number ('10x citations'), an anonymous client ('a Fortune 500 SaaS'), and no link to a live engagement page — treat it as marketing copy, not evidence. Real outcomes have specific numbers, named programme scope, and a verifiable URL. 02 / 06 Chapter ## Case 1: Media Company AI SEO Rewrite — +2,092% Clicks in 12 Months In short An Alice Labs media client rewrote 178 articles using AI-driven SEO and LLMO tactics. Over 12 months, clicks grew from 141 to 3,091 — a +2,092% increase on 8.77M impressions. The engagement was a content-rewriting programme. The client's existing article inventory was ranking but not earning clicks — classic mid-position decay against AI Overviews and shifting SERP layouts. Alice Labs ran 178 articles through an AI-augmented rewrite pipeline. Each rewrite added the patterns Aggarwal et al. (2024) flagged as effective in generative engines — specific statistics, named-source citations, structured answers near the top of each piece. **Programme scope.** 178 articles, 12 months, single content domain, AI-augmented rewriting with editorial review on every piece before publication. **Baseline.** 141 monthly clicks at programme start. The same article inventory was already indexed and ranking — the rewrite addressed CTR and citation quality, not ranking from scratch. **Outcome.** 3,091 monthly clicks by month 12 — a +2,092% increase on 8.77 million total impressions across the rewritten article set. The rewrite economics are documented in full on the engagement page. [Read the full Media Company AI SEO Rewrite case →](/en/case/media-company-ai-seo-rewrite) What the +2,092% number actually proves The lift is not a magic-bullet metric. It is the compounding effect of rewriting at scale (178 articles) with verified LLMO tactics applied consistently. The engagement page documents the full content scope and timeline. alicelabs.ai/en/case/media-company-ai-seo-rewrite 03 / 06 Chapter ## Case 2: Accounting Firm AI SEO — 0 to 41 Top-3 Rankings in 3 Months In short An Alice Labs accounting firm engagement rebuilt search authority from a near-zero base. Over 3 months, top-3 Google rankings grew from 0 to 41, organic traffic rose +260%, and monthly visitors grew from 2,500 to 9,000+. This was a brand-authority rebuild, not a tactical sprint. The client was a regional accounting firm with limited search presence outside its core branded queries. The engagement combined LLMO foundations — entity clarity, Schema.org markup, citation-rich content — with topical authority work on the question patterns AI Overviews now dominate (advisory, regulatory, deadline-driven queries). **Programme scope.** 3 months, single market, full content authority rebuild on advisory and regulatory topics, with structured-data and entity-graph cleanup. **Baseline.** 0 top-3 Google rankings outside branded queries. ~2,500 monthly organic visitors. Almost no citation share inside ChatGPT, Perplexity, or AI Overviews answers on category-defining prompts. **Outcome.** 41 top-3 Google rankings inside 3 months. Organic traffic +260% (2,500 → 9,000+ monthly visitors). The shift was visible inside ChatGPT and AI Overviews answers as well — the same content patterns that move Google also moved citation share. [Read the full Accounting Firm AI SEO case →](/en/case/accounting-firm-ai-seo) Why the SEO + LLMO overlap matters The accounting case is the cleanest example of why LLMO and traditional SEO are co-benefits. The same content investment (entity clarity, schema, citation-rich answers) moved both Google rankings and AI citation share — without separate workstreams. 04 / 06 Chapter ## Case 3: Media Company Content Production — 1M SEK First-Year Revenue In short An Alice Labs media client built a content-production engine that turned into a self-sustaining revenue stream. First-year revenue reached 1M SEK and scaled to 400k SEK/month within 12 months. This case is the production-discipline version of the LLMO story. The first two cases prove LLMO tactics work. This one proves that the bottleneck for most programmes is execution capacity, not strategy. The engagement set up a repeatable content-production pipeline with AI in the loop — editorial planning, drafting, citation review, and publication cadence. Over 12 months, the pipeline output became commercially monetizable in its own right. **Programme scope.** 12 months, content-production pipeline build, with AI-augmented drafting and editorial review on every piece. The deliverable was throughput, not a single content artefact. **Baseline.** Limited monetizable content production. Editorial capacity was the binding constraint — classic media-company economics where strategy is clear but throughput is not. **Outcome.** 1M SEK first-year revenue from the content stream. Scaled to 400k SEK per month within 12 months. Self-sustaining — the pipeline pays for itself and funds its own continued production. [Read the full Media Company Content Production case →](/en/case/media-company-content-production) Production rate is the hidden ROI variable Most failed LLMO programmes fail on execution discipline, not strategy. The media production case is the strongest internal proof that throughput compounds — and that AI-augmented production capacity is the ROI variable most boards under-invest in. ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## See how Alice Labs would approach your category The six cases in this article span media, accounting, food, public sector, security, and publishing. A 30-minute discovery call frames how the same engagement structure would apply to yours. [Request a Discovery Call](#contact) 05 / 06 Chapter ## The Pattern: Aggarwal's Citation Tactics, Applied at Scale In short Every Alice Labs LLMO win shares the same underlying pattern: the citation/statistic/quotation tactics validated by Aggarwal et al. (2024, arXiv:2311.09735) applied consistently across a large content set. The lift is from compounding, not from any single tactic. The six cases span industries — media, accounting, food, public sector, security, content. The mechanics differ. The underlying pattern does not. Aggarwal et al. (2024) tested nine content modifications across multiple generative engines (arXiv:2311.09735). The winners were specific — adding inline citations, named statistics, and quotation from authoritative sources. Those patterns delivered up to 40% visibility lift inside generative engines. Every Alice Labs LLMO engagement applies the same three patterns. The lift comes from scale and consistency, not from any single piece of content. - **Inline citations.** Every claim sourced to a named authority, dated where possible. This is the single biggest LLMO content-quality move. - **Specific statistics.** Concrete numbers in context — "+2,092%", "83% cost reduction", "6,400-8,000 hours/year" — rather than vague "significant" or "major". - **Authoritative quotation.** Named sources, dated work, peer-reviewed studies. Aggarwal's own paper is the clearest example we cite repeatedly. What makes the pattern repeatable is the production discipline behind it. The media rewrite case rewrote 178 articles. The content-production case shipped consistently for 12 months. The accounting case sustained authority work across 3 months. Tactics without throughput do not move metrics. That is the single biggest takeaway from looking across all six engagements — the strategy is published in Aggarwal's paper, but the production discipline is what separates programmes that compound from programmes that stall. The repeatable LLMO formula Aggarwal's three patterns (inline citations + named statistics + authoritative quotation) applied across a sufficient content volume, with editorial review for citability before publish. That is the formula behind every verified Alice Labs LLMO outcome in this article. ### Want to discuss how this applies to your organization? Book a free 30-minute strategy call with our AI team. [Book a call](/en/ai-consulting-services#contact-form) 06 / 06 Chapter ## How Alice Labs Structures LLMO Engagements In short Alice Labs LLMO engagements follow a four-phase structure: citation benchmark, content audit, AI-augmented production sprint, and ongoing measurement. The structure is the same across industries; the scope adjusts to client capacity. The six cases differ in industry, scope, and outcome metric. They share the same engagement structure. Every LLMO programme Alice Labs ships goes through the same four phases. **Phase 1 — Citation benchmark.** Fixed prompt set across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews on category-defining queries. Baselines the share of voice today. Repeats quarterly to track progress. **Phase 2 — Content audit.** Existing inventory scored on the Aggarwal pattern fit. Identifies which pieces are rewrite candidates, which need fresh content, and which need entity/schema cleanup before any content work begins. **Phase 3 — AI-augmented production sprint.** The longest phase. Rewrites or new content shipped at the cadence the client capacity allows — 178 articles in the media case, sustained throughput in the content-production case, focused authority work in the accounting case. **Phase 4 — Ongoing measurement.** Quarterly citation benchmark repeat plus GSC and Google Trends tracking on branded search lift. Outcomes are reported with the same metric framing used in the original baseline. What varies between engagements is depth, not structure. A three-month accounting engagement and a twelve-month media engagement run the same phases — the production phase scales to client capacity and category complexity. One implication for any internal team. The single biggest controllable variable is production throughput in Phase 3. Programmes that under-resource production consistently underperform, regardless of strategy quality. Scope adjusts; structure does not The same four phases (benchmark, audit, production sprint, measurement) run on every engagement — from a 3-month accounting authority rebuild to a 12-month media rewrite programme. Scope and depth scale to client capacity, but the structure is consistent. Alice Labs Client Outcomes — six verified LLMO and adjacent AI engagements Case Industry Approach Verified Outcome Media company AI SEO rewrite Media / publishing AI-driven rewriting of 178 articles with LLMO citation patterns +2,092% clicks (141 → 3,091); 8.77M impressions in 12 months Ljusgårda (Supernormal Greens) Food & grocery AI-augmented operations replacing manual workflows 2.5M SEK/yr savings; 83% cost reduction; 6 → 1 FTE in 6 weeks Public Sector Document Automation Public sector Document workflow automation with AI in the loop 6,400-8,000 hours/yr freed; 60h → 3 min per document Global Security Firm Marketing Automation Security / B2B 7-channel marketing automation across the funnel 176K SEK/mo savings; 7 channels automated; 2+ FTE freed monthly Accounting Firm AI SEO Professional services SEO + LLMO authority rebuild across advisory queries 0 → 41 top-3 rankings in 3 months; +260% traffic; 2,500 → 9,000+ visitors Media Company Content Production Media / publishing AI-augmented content production pipeline build 1M SEK first-year revenue; scaled to 400K SEK/mo within 12 months Source: Alice Labs proprietary client engagements (verified — see /en/case/\* links) ## About the Authors & Reviewers Published May 6, 2026 · Updated May 13, 2026 Written by ![Linus Ingemarsson - Co-Founder, Alice Labs at Alice Labs](/images/linus-ingemarsson.png) [Linus Ingemarsson](https://www.linkedin.com/in/linus-ingemarsson/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Author of 7 research reports on AI adoption, governance and labor markets cited across EU, OECD and US benchmarks. - 8+ years in AI strategy & implementation - Top-5 AI Speaker, Sweden (Mindley 2025) - 100+ enterprise AI engagements [View profile](https://www.linkedin.com/in/linus-ingemarsson/) [](https://www.linkedin.com/in/linus-ingemarsson/)[](mailto:linus@alicelabs.ai) Reviewed by May 13, 2026 ![Eric Lundberg - Co-Founder, Alice Labs at Alice Labs](/images/eric-lundberg.png) [Eric Lundberg](https://www.linkedin.com/in/eric-lundberg-3530451bb/) Co-Founder, Alice Labs Co-Founder at Alice Labs. Builds AI automation, agent workflows and integration systems that hold up in real business operations. - AI automation & agent systems lead - Workflow design across 100+ deployments - Specialist in RAG, integrations & APIs [View profile](https://www.linkedin.com/in/eric-lundberg-3530451bb/) [](https://www.linkedin.com/in/eric-lundberg-3530451bb/)[](mailto:eric@alicelabs.ai) Published May 6, 2026 · Updated May 13, 2026 Reviewed for technical accuracy, methodology and source integrity. · All claims trace to public sources cited in-line. ## Frequently Asked Questions ### What is an LLMO case study? An LLMO case study is a documented client engagement showing measurable outcomes from large language model optimization. Credible LLMO case studies include named programme scope, dated baselines, specific (non-round) numbers, and a link to the underlying engagement page. Without all four, the case study is marketing copy, not evidence. ### Why are most public LLMO case studies fake? Most published LLMO case studies cite anonymous 'Fortune 500' clients with round-number outcomes (10x, 5x) and no verifiable link. That pattern is a marketing tell, not a measurement one. Genuine cases use specific numbers like +2,092%, name the programme type, and link to a live engagement page documenting the work. ### What was the +2,092% Alice Labs media case actually? An Alice Labs media client rewrote 178 articles via an AI-augmented pipeline that applied the Aggarwal et al. (2024) citation patterns. Clicks grew from 141 to 3,091 (+2,092%) over 12 months across 8.77M total impressions. The full case is documented at /en/case/media-company-ai-seo-rewrite. ### How did the accounting firm reach 41 top-3 rankings in 3 months? The engagement was a search authority rebuild combining LLMO foundations (entity clarity, Schema.org markup, citation-rich content) with topical authority work on advisory and regulatory queries. Over 3 months, top-3 rankings grew 0 → 41 and traffic +260% (2,500 → 9,000+ monthly visitors). Full case: /en/case/accounting-firm-ai-seo. ### What is the repeatable pattern across Alice Labs LLMO wins? Every case applies the three Aggarwal et al. (2024) citation patterns — inline citations, named statistics, authoritative quotation — across sufficient content volume with editorial review before publish. The lift comes from scale and consistency, not from any single tactic. Production throughput is the binding constraint on most underperforming programmes. ### How long does an LLMO engagement take to show results? Citation share moves first — usually within the first quarter for well-executed programmes. The accounting case showed top-3 ranking growth within 3 months. The media rewrite case compounded over 12 months. Production capacity is the rate-limiting variable: programmes with constrained editorial throughput take proportionally longer. ### Can these LLMO patterns work outside the Nordics? Yes. The Aggarwal et al. (2024) tactics are platform-level, not market-level — they describe how generative engines select and rank citations across language and geography. Alice Labs cases are Nordic-rooted but the underlying mechanics (inline citations, named statistics, authoritative quotation, production discipline) transfer to any market. ### How do I verify the Alice Labs case study numbers? Every case in this article links to a live /en/case/\* engagement page on alicelabs.ai. The numbers used here are the same numbers we report in client-facing work and sales conversations. If you need additional verification for procurement or board purposes, request a reference call via /en/ai-search. [Previous in AI Search & LLMO ### How to Get Cited by Perplexity AI: Complete 2026 Playbook ](/en/insights/how-to-get-cited-by-perplexity-ai)[Next in AI Search & LLMO ### LLMO for B2B Enterprise: 4-Phase Playbook for 2026 ](/en/insights/llmo-for-b2b-enterprise) ## Further reading - [GEO: Generative Engine Optimization (Aggarwal et al., 2024)](https://arxiv.org/abs/2311.09735)· arxiv.org - [llms.txt — Answer.AI proposal (Jeremy Howard, Sep 2024)](https://llmstxt.org)· llmstxt.org ## Related reading [pillar ### AI Search Optimization: Complete Guide for 2026 Pillar guide for the LLMO discipline these case studies draw from. 14 min](/en/insights/ai-search-optimization-guide) [deepdive ### Citation Optimization for AI Search Engines Aggarwal's citation tactics applied to enterprise content programmes. 10 min](/en/insights/citation-optimization-ai) [deepdive ### LLMO Content Strategy — From Audit to Production The four-phase Alice Labs engagement structure behind every case in this article. 12 min ](/en/insights/llmo-content-strategy) ## Sources 1. [Alice Labs case study — Media Company AI SEO Rewrite (+2,092% clicks; 178 articles; 8.77M impressions)](https://alicelabs.ai/en/case/media-company-ai-seo-rewrite)(accessed 2026-05-06) 2. [Alice Labs case study — Ljusgårda (Supernormal Greens) — 2.5M SEK/yr savings; 83% cost reduction; 6 → 1 FTE](https://alicelabs.ai/en/case/ljusgarda-ai-agent)(accessed 2026-05-06) 3. [Alice Labs case study — Public Sector Document Automation (6,400-8,000 hrs/yr; 60h → 3 min per doc)](https://alicelabs.ai/en/case/public-sector-document-automation)(accessed 2026-05-06) 4. [Alice Labs case study — Global Security Firm Marketing Automation (176K SEK/mo savings; 7 channels; 2+ FTE freed)](https://alicelabs.ai/en/case/security-company-marketing-automation)(accessed 2026-05-06) 5. [Alice Labs case study — Accounting Firm AI SEO (0 → 41 top-3 rankings; +260% traffic; 2,500 → 9,000+ visitors)](https://alicelabs.ai/en/case/accounting-firm-ai-seo)(accessed 2026-05-06) 6. [Alice Labs case study — Media Company Content Production (1M SEK first year; 400K SEK/mo within 12 months)](https://alicelabs.ai/en/case/media-company-content-production)(accessed 2026-05-06) 7. [Aggarwal et al. — GEO: Generative Engine Optimization (arXiv:2311.09735, 2024)](https://arxiv.org/abs/2311.09735)(accessed 2026-05-06) 8. [llms.txt — Answer.AI proposal (Jeremy Howard, September 2024)](https://llmstxt.org)(accessed 2026-05-06) 9. [SparkToro / Datos — 2024 zero-click search analysis (~60% of Google searches end without an open-web click)](https://sparktoro.com/blog/in-2024-we-finally-satisfyingly-measured-how-much-of-google-search-traffic-goes-to-google-properties/)(accessed 2026-05-06) Next scheduled review: 2026-08-11 ![Linus Ingemarsson](/images/linus-ingemarsson.png)![Eric Lundberg](/images/eric-lundberg.png)![Alice Holmgren](/images/alice-holmgren.png) Alice Labs practitioner team ## Want an LLMO case study of your own? Alice Labs runs the same four-phase engagement (benchmark, audit, production, measurement) that produced the six cases above. The discovery call models scope, capacity, and likely outcome timing against your category. [Book an AI Search Discovery](#contact) Share [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fllmo-case-studies)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Falicelabs.ai%2Fen%2Finsights%2Fllmo-case-studies&text=LLMO%20Case%20Studies%3A%20Brands%20Winning%20in%20AI%20Search%20\(2026\)) ## Get in Touch! The lab usually responds within 24 hours. 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