Updated January 08, 2026
TL;DR: A $25M ARR project management SaaS was invisible in AI search, appearing in only 8% of buyer queries while competitors dominated 65% of ChatGPT and Perplexity recommendations. After partnering with Discovered Labs to implement our
CITABLE framework for AI content optimization and shift to daily content production, they reached a 24% citation rate in 90 days. The result: 47 AI-referred leads converting at 2.8x the rate of traditional organic search traffic, generating over €180K in projected pipeline value.
Organic leads were declining 22% quarter-over-quarter despite consistent SEO investment. Google Search Console showed steady traffic, strong domain authority, and page-one rankings for target keywords.
Yet when the VP of Marketing tested buyer queries in ChatGPT, the results were brutal: five competitor recommendations, zero mention of her company.
Research shows that 66% of B2B senior decision-makers now use AI tools including ChatGPT, Copilot, and Perplexy to research and evaluate suppliers. For this $25M ARR project management SaaS with 180 employees, that meant more than half of potential buyers were making shortlists that excluded them entirely.
When the board asked "What's our AI strategy?", she didn't have an answer.
This case study details how we reversed that invisibility in 90 days.
The invisible problem: Why traditional SEO failed to trigger AI citations
The company's content marketing followed standard SEO playbook tactics. They published 8-12 blog posts monthly, optimized for keyword density, built backlinks from relevant sites, and tracked rankings religiously. Google Search Console showed steady traffic, but AI search platforms ignored them completely.
Our AI visibility audit tested 100 high-intent buyer queries across ChatGPT, Claude, Perplexity, and Google AI Overviews. The findings revealed a critical gap.
Citation rate: 8%
The company appeared in AI-generated answers for only 8 of 100 buyer-intent queries. Competitors appeared in 65% of the same queries, often with specific feature comparisons and use case recommendations.
The root cause: Their content was optimized for search engine algorithms from 2018, not for large language model retrieval in 2026. Blog posts focused on keyword placement rather than entity clarity. Long-form articles buried answers deep in paragraphs. Claims lacked verifiable data sources.
According to Forrester research, B2B buyers adopt AI-powered search at three times the rate of consumers, with 89% of B2B buyers having adopted generative AI as a key source throughout their purchasing journey.
Generative Engine Optimization (GEO) structures content specifically for citation in AI-generated answers. Unlike SEO (which targets ranking positions in search results), GEO focuses on passage retrieval where LLMs extract and cite specific content blocks that directly answer buyer questions.
The company needed a fundamental shift from content marketing to knowledge engineering.
The GEO agency strategy: Implementing the CITABLE framework
Traditional content agencies repurpose SEO tactics for AI optimization. They add "AI-friendly" to their service descriptions without changing methodology. Specialized GEO agencies engineer content specifically for LLM retrieval systems.
We implemented our CITABLE framework to address each technical requirement for AI citation:
Clear entity & structure: We rewrote product pages to open with 2-3 sentence BLUF (Bottom Line Up Front) answers. LLMs prioritize content that states the main point immediately, not marketing copy that buries key facts.
Intent architecture: We mapped content to actual buyer questions identified in the audit - comparison queries like "Asana vs [Company]" and use case queries like "project management for distributed teams." Each high-volume question cluster got a dedicated answer page.
Third-party validation: We secured 47 mentions on Reddit through our dedicated Reddit marketing service, encouraged 23 new G2 reviews from customers, and earned citations in relevant industry forums. LLMs cross-reference multiple sources before citing a brand, so distributed mentions build credibility.
Block-structured for RAG: LLMs use Retrieval-Augmented Generation (pulling discrete content blocks to construct answers). We broke long articles into 200-400 word sections with clear H3 headings, used tables for feature comparisons, and formatted FAQs as explicit question-answer pairs.
The framework provided structure, but execution required velocity. Daily content production became the primary driver of citation rate growth.
Execution timeline: From audit to 24% citation rate in 90 days
Weeks 1-2: Audit & quick win identification
The initial visibility audit tested queries across five platforms and generated a baseline report showing exactly where competitors appeared and this company didn't. We identified eight "quick win" queries where they had relevant content needing only structural optimization to trigger citations.
We set up weekly citation tracking using internal tools that automate query testing across ChatGPT, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot. This created a measurable share of voice metric the VP could report to leadership.
Weeks 3-4: The velocity shift
The company had been publishing four blog posts monthly. We shifted to five content pieces weekly - 20+ pieces monthly - using our end-to-end content production system.
Frequency signals topical authority to LLMs because consistent publishing on a topic indicates expertise. Each piece targeted a specific buyer question identified in the audit.
By week four, citation rate grew from 8% to 12%.
"Seeing our company recommended by ChatGPT for the first time was surreal. We'd been invisible for 18 months despite solid SEO work." - VP of Marketing, Project Management SaaS
Weeks 5-12: Third-party validation & optimization
We launched a concentrated Reddit marketing campaign using aged, high-karma accounts to mention the platform in relevant subreddit discussions about project management tools. We also ran a G2 review campaign, securing 23 new reviews from existing customers.
This third-party validation created the consensus signals LLMs need to confidently cite a brand. AI models prioritize sources mentioned consistently across multiple platforms over brands that appear only on their own website.
Weekly citation reports showed which content formats drove citations most effectively. Tables and bulleted feature lists got cited more often than narrative paragraphs. We doubled down on high-performing formats and expanded to adjacent query clusters.
By day 90, citation rate reached 24% - exactly 3x the starting baseline.
Results analysis: 47 AI-referred leads and 2.8x higher conversion
The 8% to 24% citation rate improvement translated directly into measurable pipeline impact over the 90-day period.
Primary metric: 24% citation rate across platforms
The company now appeared in ChatGPT, Claude, or Perplexity recommendations for 24 of 100 tested buyer-intent queries. This placed them in initial consideration sets alongside competitors who had previously dominated AI recommendations alone.
Google AI Overviews cited them in 31% of queries where featured snippets appeared. ChatGPT cited them in 19% of tested queries. Perplexity showed the strongest performance at 28% because the platform prioritizes recent, well-structured content - exactly what our CITABLE framework delivers.
Pipeline impact: 47 qualified leads from AI referral traffic
Analytics tracking showed 47 leads arriving with referral sources from ChatGPT, Perplexity, or Claude during the 90-day period. Prior to the engagement, AI-referred traffic was effectively zero - occasional single-digit monthly visitors who rarely converted.
These 47 leads weren't just traffic. They converted to sales qualified opportunities at significantly higher rates than traditional organic search traffic.
Conversion advantage: 2.8x higher than traditional search
Leads arriving from AI referral sources converted to qualified opportunities at 18.7% compared to 6.7% for traditional organic search traffic. This 2.8x conversion advantage matches broader industry findings.
Ahrefs research published in 2025 found AI search visitors convert at dramatically higher rates, with their own platform seeing visitors from AI search drive conversions at 23 times the rate of traditional organic traffic.
The quality difference makes sense. Buyers who ask AI for recommendations have already described their requirements, constraints, and use case in the prompt. When an AI system recommends a specific solution, the buyer arrives pre-qualified and further along in their evaluation process.
"We went from invisible to recommended in 90 days. These aren't tire-kickers - they're qualified prospects who've already been told we're a good fit for their needs. The conversion rate difference is the real story here." - VP of Marketing, Project Management SaaS
The combination of 3x citation rate growth and 2.8x conversion advantage created significant pipeline impact within a single quarter.
Calculating the ROI of investing in specialized GEO agency services
Marketing leaders need to justify new agency spend with clear ROI projections. The math for this engagement shows strong returns within the first 90 days.
Investment: €5,495 monthly
The company invested €5,495 per month in our standard retainer (AI visibility auditing, daily content production at 20+ pieces monthly, Reddit marketing, technical optimization, and weekly citation tracking). Over 90 days, total investment was €16,485.
Return: 47 qualified leads with 2.8x conversion advantage
The 47 AI-referred leads converted at 18.7% to qualified opportunities, producing 8 sales qualified opportunities in 90 days. The company's average deal size is €32,000 with a 28% close rate. Eight opportunities at 28% close rate yields 2 closed deals.
Pipeline value: €64,000 in closed revenue
Two deals at €32,000 average deal size equals €64,000 in closed revenue directly attributable to AI visibility improvement in the first 90 days. Additional opportunities remain in pipeline with projected close dates in Q1 2026.
"Our CFO asked for ROI proof after 60 days. Showing €64K in closed revenue from a €16K investment made the board conversation easy." - VP of Marketing, Project Management SaaS
Cost per lead: €350
At €16,485 investment for 47 leads, the cost per lead was €350 - demonstrating efficient lead acquisition compared to other digital marketing channels.
ROI: 288% in 90 days
€64,000 in closed revenue from €16,485 investment equals 288% ROI, or a 3.9x return, within a single quarter. This doesn't account for the longer-term compounding effect. The content and citations continue generating leads beyond the initial 90-day period.
GEO vs SEO: Why generalist agencies struggle with AI visibility
Marketing leaders evaluating GEO agencies often ask whether their current SEO agency can simply "add AI optimization" to existing services. The methodology difference makes that transition difficult.
| Capability |
Traditional SEO Agency |
Specialized GEO Agency (Discovered Labs) |
| Primary metric |
Keyword rankings, domain authority |
Citation rate, share of voice in AI answers |
| Content velocity |
8-12 pieces monthly |
20+ pieces monthly (daily production) |
| Content structure |
Keyword-optimized long-form |
CITABLE framework for LLM retrieval |
| Tracking tools |
Semrush, Ahrefs, Moz (rank tracking) |
Internal AI visibility tracking, LLM query testing, multi-platform citation monitoring |
Methodology mismatch: SEO agencies optimize for search engine crawlers that index and rank pages. GEO agencies engineer for LLM retrieval systems that extract and cite specific passages.
The difference isn't semantic. It's technical. Content that ranks well often doesn't get cited because it lacks the entity clarity, verification, and structure LLMs need.
Measurement gap: Traditional SEO reports show ranking improvements and traffic growth. GEO requires tracking citation rates across multiple AI platforms through systematic query testing. Most SEO agencies lack the tooling to measure AI visibility, much less optimize for it.
Velocity difference: Daily content production drives citation rate growth because LLMs interpret publishing frequency as a topical authority signal. Traditional agencies deliver 2-3 blog posts weekly at most.
Gartner predicts traditional search engine volume will drop 25% by 2026 as AI chatbots capture buyer research. Companies need specialized expertise built for this shift, not adapted from the previous era.
Checklist: How to evaluate a GEO agency for B2B SaaS
Marketing leaders considering GEO agency partners should evaluate capabilities that directly impact citation rates and pipeline outcomes. Use this checklist to assess vendors:
1. Do they have a proprietary methodology for AI citation?
Generic "we optimize for AI" claims don't cut it. Look for agencies with named frameworks, published methodologies, and specific tactics. We use the CITABLE framework.
2. Can they track citations across multiple AI platforms?
AI visibility requires systematic measurement across ChatGPT, Claude, Perplexity, Google AI Overviews, and Copilot. Ask for sample citation tracking reports.
3. Do they offer daily content production velocity?
Frequency drives authority signals for LLMs. Agencies delivering 8-12 pieces monthly won't generate the volume needed for citation rate growth.
4. Do case studies show pipeline metrics, not just traffic growth?
Citation rates matter only if they generate qualified leads. Look for case studies showing citation rate improvement AND corresponding pipeline impact with specific lead counts, conversion rates, and revenue - not just traffic growth or ranking improvements.
5. What contract terms do they require?
Month-to-month terms signal confidence in results. Agencies demanding 12-month contracts or annual prepayment don't trust their methodology enough to earn your business monthly. We offer rolling monthly contracts because clients see measurable progress within 30-45 days.
Your next 30 days: From invisible to cited
The companies winning in AI search aren't outspending competitors on content. They're engineering knowledge graphs, securing third-party consensus, and structuring information specifically for LLM retrieval systems. That's the difference between visibility and invisibility when 66% of B2B buyers ask AI for recommendations.
This project management SaaS reversed a 22% quarterly lead decline in 90 days using our CITABLE framework and daily content production. The result: €64,000 in closed revenue from AI-referred leads converting at 2.8x traditional search rates - 288% ROI in a single quarter.
Your competitors' AI visibility advantage grows each quarter they appear in recommendations while you don't. According to G2's 2025 research, nearly 8 in 10 B2B buyers say AI search has changed how they conduct research, with 29% now starting vendor research via platforms like ChatGPT more often than Google.
Find out exactly where you're visible and where competitors dominate AI recommendations. Book a free AI visibility audit with Discovered Labs - we'll test 50-100 buyer-intent queries across ChatGPT, Claude, Perplexity, and Google AI Overviews, then show you the specific citation gaps costing you pipeline.
Book your AI visibility audit and get your baseline citation rate within one week.
FAQs
How long does it take to see results from GEO? Initial citation signals typically appear within 3-4 weeks of implementing structured content optimized for LLM retrieval. Measurable pipeline impact (qualified leads from AI referral traffic) becomes evident at 60-90 days as citation rates reach 20-30% of target buyer queries.
Does GEO replace traditional SEO or complement it? GEO complements SEO by optimizing for AI-powered answer engines while SEO continues capturing traditional search traffic. Both channels serve different stages of the buyer journey, with GEO addressing early research and SEO handling deeper product evaluation.
What citation rate should B2B SaaS companies target? Start with 20-25% citation rates within 90 days as your initial benchmark. Strong performers reach 40-50% at six months with consistent content production and third-party validation. Top performers in competitive categories can achieve 60%+ citation rates by month 12.
How do you attribute pipeline to AI visibility improvements? We track referral sources from AI platforms through UTM parameters and custom Salesforce fields that identify AI-referred leads. Weekly reports show AI-sourced opportunities isolated from other channels, with conversion rate comparisons against traditional organic search to demonstrate the 2-3x quality advantage.
Key terms glossary
Generative Engine Optimization (GEO): The process of structuring content specifically for citation in AI-generated answers from platforms like ChatGPT, Claude, and Perplexity. GEO focuses on entity clarity, verification, and passage retrieval rather than keyword rankings.
Citation rate: The percentage of times a brand appears in AI-generated answers across a defined set of buyer-intent queries. A 24% citation rate means the brand was mentioned in 24 of 100 tested queries relevant to their category.
CITABLE framework: Discovered Labs' proprietary methodology for optimizing content for LLM citation, covering Clear entity structure, Intent architecture, Third-party validation, Answer grounding, Block structure for RAG, Latest and consistent information, and Entity relationships.
Share of voice (AI context): The proportion of AI citations a brand receives compared to competitors across target buyer queries. If five brands compete for citations and you appear in 30% of answers, you hold approximately 30% share of voice in that category.
