SEO & AI Success Story

How One AEO Strategy Doubled a B2B SaaS Pipeline to $1.5M in 18 Months

How Qrvey used a unified AEO and organic search strategy — golden keywords, LLM prompt mapping, Reddit citation, and structured content — to grow pipeline from $740K to $1.5M.

Why we curate this: SEO and AI search visibility are evolving fast, leaving many growth teams guessing. Uprankly curates real-world breakdowns from public sources to show what actually produces results — not theory.

1. The Result at a Glance

Starting point: Qrvey — an embedded analytics platform — had been burned by traditional SEO agencies. Competitive low-volume B2B niche. Organic wasn't producing pipeline.
Outcome: Pipeline grew from $740K to $1.5M from organic alone. 30% of closed business traced directly to organic search.
Timeframe: 18 months. Organic clicks up 400% in the first 6 months.
Core insight: In a low-volume, high-ACV B2B niche, chasing search volume is the wrong game. The real opportunity is owning the exact phrases buyers type into both Google and AI chat — and making sure every source those AI tools read says the same thing about you. Google's guidance on creating helpful, people-first content reinforces this — quality signals are evaluated domain-wide, not page by page.

2. Step-by-Step Execution: What They Did

1. Isolated commercial "golden keywords" over broad volume

Davies ignored high-volume informational terms and mapped only the queries that indicate active buying intent — the specific phrases a VP of Engineering types when they're actually evaluating tools. In a niche like embedded analytics, total monthly search volume is low. Every high-intent click matters more than a thousand informational ones. Search Engine Land's breakdown of search intent classification explains exactly why commercial intent queries convert at multiples of informational ones.

2. Mapped search terms to the actual AI prompts buyers use

Each target keyword was translated into its conversational AI equivalent. "Embedded analytics for SaaS" became "What's the best embedded analytics platform for SaaS companies wanting to add dashboards?" This matters because buyers don't always search — they ask. An AEO strategy has to cover both entry points or it's leaving half the funnel unaddressed. Search Engine Journal's guide to mastering SERP analysis covers how to reverse-engineer these dual intent signals from both traditional and AI-powered results.

3. Audited what ChatGPT and Perplexity were already citing

Before building anything new, Davies identified which specific sources — blogs, comparison pages, review aggregators — were being pulled into AI-generated answers for Qrvey's target prompts. That audit became the prioritized list for outreach and content. You can't get cited by AI if you're not present in the sources it already reads. This is the practical application of what Search Engine Land calls Google's Information Gain scoring — the sources AI trusts are the ones already adding unique, verifiable value.

4. Built structured content designed for both Google and LLM extraction

Comparison tables, alternative pages, and case studies were created in formats that LLMs can parse and extract from cleanly. Not just readable — structured for RAG. Google's introduction to structured data and rich results outlines why machine-readable formatting matters for both traditional search features and AI extraction. This content served double duty: it ranked in Google and got pulled directly into AI-generated answers.

5. Used Reddit as a genuine citation source, not a traffic channel

Davies engaged in relevant subreddits with substantive answers — recommending Qrvey where it was a genuine fit. The result: 100% of organic social demo requests came through Reddit. These threads also get scraped by LLMs. A well-placed answer in the right community thread builds both trust with real buyers and citation weight with AI engines. Search Engine Land's guide to optimizing anchor text reinforces why the surrounding context of a mention — not just the link itself — is what drives both search and AI value.

6. Acquired contextual authority links to commercial pages

Targeted link building from SaaS and B2B publications pointed directly at commercial landing pages — not the homepage, not the blog. This gave those pages the authority signal needed to rank for commercial terms, which in turn increased the probability of AI citation. Moz's guide to link equity and how redirects pass authority explains the mechanics behind why directing links to the right page matters more than raw link volume.

3. What NOT to Do: Pitfalls & Common Mistakes

Chasing keyword volume in a low-volume, high-ACV niche. If the average deal is $50K+, ten high-intent visitors per month outperforms ten thousand browsers. Optimizing for volume metrics in this context pulls resources toward content that attracts the wrong audience.
Optimizing for Google while ignoring AI citation sources. A page can rank on page one and still never appear in a ChatGPT or Perplexity answer if the sources those models actually read don't reference your brand. Traditional SEO and AEO are not the same problem.
Spamming Reddit with low-value links. Dropping a link without context doesn't just fail — it gets flagged and removed, and it signals to the community (and eventually to AI scrapers) that your brand operates that way. The only Reddit play that works is one where you'd be comfortable with your name attached to the reply.
Building content without comparison and alternative structure. Buyers in any software category will search "Qrvey vs Competitor" and "Qrvey alternatives" before making a final decision. If you don't own those pages, a competitor does. LLMs pull heavily from comparison content because it's structured and information-dense — Ahrefs' guide to building comparison pages that convert covers the exact formatting that works.
Treating AEO as a one-time content sprint. AI models update their retrieval indexes. A cluster of content published in Q1 can lose citation coverage by Q3 if mentions aren't maintained and backlinks aren't monitored. This is an ongoing program, not a campaign.

4. How to Implement This Strategy (And How Uprankly Helps)

Step 1: Map your commercial keywords and their AI prompt equivalents

List every high-intent search query a buyer in your category would use at the evaluation stage. Then translate each one into the conversational prompt they'd type into ChatGPT or Perplexity. These two lists are your keyword strategy and your AEO brief combined.

How Uprankly helps: Use Link Planner's competitor backlink footprint analysis to identify which commercial pages in your category are already attracting authority — that's your map of what search intents matter most.

Step 2: Audit which sources AI is already citing for your target prompts

Run your target prompts through ChatGPT and Perplexity. Note every domain cited in the answers. That list is your prioritized outreach queue — get on those sites before you build anything else.

How Uprankly helps: Link Planner's domain gap audits show which high-authority domains are linking to competitors but not to you — a strong proxy for the same sources AI is already reading and trusting.

Step 3: Build comparison and alternative pages structured for LLM extraction

Create pages for "[Your Product] vs [Competitor]" and "[Competitor] alternatives" with comparison tables, clear use case breakdowns, and structured data. Format for skimmability — both human readers and RAG systems extract from well-structured pages faster.

How Uprankly helps: Use Link Builder's guest post outreach campaigns to acquire placements on high-authority B2B and SaaS publications pointing directly at these commercial pages — not your homepage.

Step 4: Run a Reddit citation campaign the right way

Identify the subreddits where your buyers are active. Spend time in threads before dropping any recommendations. When you do recommend your product, lead with the specific use case — explain why it fits, not just that it exists. Track every thread where your brand is mentioned.

How Uprankly helps: Log community mentions alongside formal backlinks in Link Builder's brand distribution tracker to monitor your total brand footprint across both formal and community sources.

Step 5: Build authority links directly to commercial pages

Guest posts, editorial inclusions, and niche directory listings should point to your comparison pages and product landing pages — not the blog. These are the pages that need ranking power to show up both in Google and in AI-generated answers.

How Uprankly helps: Search Link Builder's database of 70,000+ vetted sites to filter by SaaS and B2B niche, pull editor contacts, and run outreach campaigns targeted at your commercial URL list.

Step 6: Monitor ranking assets and defend brand mentions

A comparison page that loses its backlinks drops in rankings, which reduces its AI citation probability. A Reddit thread that gets deleted removes a community signal you'd built up. Both need active monitoring.

How Uprankly helps: Link Monitor's real-time backlink tracking watches your links and brand mentions around the clock, sending immediate alerts when placements are lost — so recovery outreach goes out before the ranking damage compounds.

5. Uprankly Breakdown: Why This Strategy Worked

Search & AI Mechanics

LLMs like ChatGPT and Perplexity construct answers by pulling from sources that appear consistently across their retrieval index — a process called Retrieval-Augmented Generation (RAG). Davies built Qrvey's brand presence across the exact sources those models read: structured comparison pages, authoritative B2B publications, and high-engagement community threads. The result was cross-source consistency — which is what gives an AI model enough confidence to name a brand in an answer. Google's documentation on how structured data helps search understand entities explains the same trust-building principle from the traditional search side. Eighteen months of disciplined execution across Google rankings and AI citation sources was enough to move pipeline from $740K to $1.5M from organic alone.

Strategic Value Over Traditional SEO

Traditional link building targets PageRank and domain authority to move rankings. That still matters here — but Answer Engine Optimization adds a second requirement: you have to be present in the specific sources AI reads when constructing answers for your buyer's prompts. The Qrvey strategy treated Google and AI search as a single unified channel, not two separate problems. Rankings drove citation. Community engagement drove both demo requests and RAG indexation. Each piece of the program reinforced the others. See how a different B2B SaaS team used content pruning to lift domain-wide quality before rebuilding — in our curated Octolens content pruning case study.

Source & Original Discussion

This case study is compiled from a LinkedIn post by Jules Davies, Founder of Scalerrs (scalerrs.com), published 2025. Read the original discussion on LinkedIn.