From Zero to ChatGPT Citations: A 90-Day B2B SaaS Case Study

The Situation at Day Zero

Series B B2B SaaS company in the DevSecOps category. The product had category awareness: practitioners in relevant communities knew the name. The problem was specific: the brand did not appear in AI-generated answers when buyers asked about DevSecOps platforms. A buyer typing "best DevSecOps platforms for mid-market" into Perplexity or ChatGPT would get a response naming three or four competitors. This brand wasn't in it.

That gap matters more than it might seem. 73% of B2B buyers use AI assistants in their purchase research. Buyers who find a vendor through an AI-generated recommendation convert at 4.4 times the rate of organic search visitors. The brand was invisible at the moment buyers were actively forming their shortlist.

Baseline measurement via Peec AI setup: zero citations in Perplexity across 20 tracked query variations. Zero in ChatGPT across the same query set. Weak presence in Google AI Overview, appearing in one of 20 tracked queries in a generic list context without brand-specific endorsement.

Starting Reddit presence: one official subreddit post from eight months prior. Promotional in tone, framed as a product announcement. It received four upvotes and two comments, both from what appeared to be company-affiliated accounts. No organic practitioner engagement. No presence in any third-party subreddit discussions in the DevSecOps, DevOps, or security space.

Starting structured data: Organization schema on the homepage. Nothing else. No FAQPage schema on service pages, no Service schema, no structured data that would help AI retrieval systems parse what the product did or for whom.

Competitor baseline: the leading competitor appeared in 6 of 20 Perplexity queries across the tracked set. Their Reddit history: 40-plus threads over 24 months across r/devops, r/netsec, r/sysadmin, and r/AskNetsec, most appearing to be authentic practitioner-level content rather than promotional posts. Their structured data: FAQPage schema on three service pages and a well-populated G2 profile with over 150 reviews.

Week 1 to 2: The Audit

Before any content was produced, the first two weeks went to understanding the landscape. This phase determines whether the subsequent work is targeted or wasted.

LLM citation audit. Ran 50-plus query variations through Perplexity, ChatGPT (with and without Browse), and Claude. The query set covered: product category terms, specific use case queries, comparison queries against named competitors, and "best X for Y" queries at different company size segments. Documented which competitors appeared in each, what sources Perplexity cited when it named them, and what content type was driving those citations. Result: competitor citations traced back primarily to Reddit threads in r/devops and r/AskNetsec, and to FAQ-structured pages on competitor sites that ranked on page one of Google for target queries.

Reddit mapping. Identified 8 subreddits with active buyer-level conversation in DevSecOps. Primary targets: r/netsec, r/devops, r/sysadmin, r/AskNetsec. Secondary: r/kubernetes, r/docker, r/programming, r/cybersecurity. Evaluated each against engagement rate, moderation strictness, Google ranking of existing threads, and buyer concentration.

Thread analysis. Found 12 existing threads across target subreddits that ranked on the first two pages of Google for the client's target queries. None mentioned the brand. Several mentioned two to four competitors. These 12 threads were prioritized for immediate action: getting brand presence into already-ranked threads was the fastest path to Perplexity citation, since those threads were already in Perplexity's retrieval pool.

Competitor analysis. The leading competitor's 6-of-20 Perplexity citation rate traced back to 4 Reddit threads and 2 FAQ-schema service pages. Their Reddit threads were genuine: specific technical details, honest tradeoffs, no promotional language. The threads had accumulated upvotes over 12 to 24 months. Building a comparable Reddit footprint in 90 days required quality over volume: well-constructed threads that would earn engagement quickly rather than thin posts at high frequency.

Schema audit. Homepage Organization schema was accurate but sparse. Three service pages had no structured data at all. The product detail page had no Service schema. FAQ content existed on the blog in prose format but lacked FAQPage schema markup. These were all addressable in week two.

Week 2 to 3: Strategy Build

Coming out of the audit, the strategy had three tracks running in parallel: Reddit content operations, structured data implementation, and Peec AI measurement setup.

Target query definition. Narrowed from 50-plus audit queries to 20 primary query probes for Peec AI tracking. These covered the full buyer journey: awareness-stage category queries, evaluation-stage comparison queries, and high-intent "best X for use case Y" queries. The 20 queries became the consistent measurement baseline for the full 90 days.

Subreddit prioritization. Focused Reddit effort on three primary communities: r/netsec and r/devops for authority and Google ranking potential, r/AskNetsec for high-intent question threads where buyers were actively asking about tools. Secondary communities were noted but deprioritized for the initial 90 days to avoid spreading effort too thin.

Content plan. Eight Reddit threads over 90 days across four subreddits. Thread types: two question-framing threads (posing a genuine question the community would engage with), two comparison threads ("we evaluated X and Y for this use case, here's what we found"), two problem-solution threads (describing a specific problem and how the team solved it), two experience-sharing threads (specific post-implementation details). Each thread designed to invite authentic replies, not to function as product placement.

Schema additions. FAQPage schema added to three service pages, pulling existing FAQ content into structured markup. Service schema added to the main service page. Organization schema updated with fuller description, service area, and founding details. Changes implemented and submitted to Google Search Console for accelerated indexing.

Peec AI baseline set. Weekly probing schedule established across 20 tracked queries for Perplexity, ChatGPT (Browse enabled), and Google AI Overview. The baseline: zero citations in Perplexity, zero in ChatGPT, one partial mention in Google AI Overview.

Month 1: Content Seeding

Weeks three through six. The first four Reddit threads went live in target subreddits. The focus was earning genuine community engagement, not placing product mentions.

Thread one went into r/devops: a question-framing post about how teams were handling security scanning in the CI/CD pipeline at scale. Specific, technically grounded, with no product mention in the original post. The thread generated 34 comments over the first 48 hours, with several practitioners describing their stack in detail. The brand was mentioned naturally in one comment as a tool one commenter had evaluated. That organic mention, in a thread the community engaged with, was more valuable than anything promotional would have been.

Thread two went into r/AskNetsec: a direct question about how teams were approaching container security in Kubernetes environments. This subreddit's question-answer format made it ideal for high-intent engagement. The thread received 22 comments, several with detailed tool comparisons. It appeared on page two of Google within 18 days of posting.

Thread three was a comparison thread in r/devops: a practitioner account describing their evaluation of two named competitors against specific criteria, with honest findings in both directions. The brand appeared as a third tool that had also been in the evaluation. This thread format, direct comparison with honest tradeoffs, consistently outperformed other formats for Perplexity citation throughout the engagement.

Thread four was a problem-solution thread in r/netsec describing a specific security audit challenge and how the team resolved it. Lower engagement than the comparison thread, but high-quality comments from senior practitioners.

Engagement results: two threads reached "hot" status in their subreddit. One thread appeared on page two of Google within 18 days. Reply architecture functioned as planned: each thread had authentic practitioner support in the first 24 hours, which signaled to Reddit's algorithm that the post had immediate community interest.

Peec AI check at Day 30: zero ChatGPT citations (expected at this stage, training data cycles haven't run). Two Perplexity citations for long-tail query variations, both tracing back to the r/AskNetsec thread that had appeared on Google page two. This confirmed the Reddit-to-Google-to-Perplexity path was functioning.

Month 2: Compounding

Weeks seven through ten. Four more Reddit threads deployed, plus G2 review seeding began.

The second batch of threads applied the learning from month one: comparison-format threads generated Perplexity citations more reliably than question threads. Three of the four month-two threads were structured as comparisons or experience-sharing with specific technical details. The fourth was an experience thread in r/sysadmin about a migration from one platform to another, which drew high engagement from practitioners who had been through similar migrations.

G2 review seeding: the client's customer success team reached out to eight existing customers and asked them to document their experience on G2. The ask was specific: describe the use case, the outcome, and any honest tradeoffs they'd encountered. All eight submitted reviews. This was not incentivized, just prompted. The reviews added eight structured, third-party, use-case-specific brand mentions that AI retrieval systems could process.

Key milestone at Day 52: brand appeared in one Google AI Overview result. The query was a mid-funnel comparison query about DevSecOps platforms for engineering teams over 100 people. The citation pulled from the FAQ schema additions made in week two. This confirmed that structured data on first-party pages contributed to AI Overview presence independently of the Reddit work.

Perplexity citations at Day 60: 7 of 20 tracked queries now surfacing the brand, up from 2 at Day 30. The jump came primarily from two sources: the Google page-two ranking threads from month one climbing to page one, and the comparison-format threads from the month-two batch being indexed and retrieved by Perplexity.

What was working: comparison-format threads outperformed question threads at roughly 3-to-1 for Perplexity citation frequency. FAQ schema additions contributed independently to Google AI Overview. G2 reviews added structured third-party signal that appeared in two Perplexity citations as a source alongside Reddit.

What wasn't working: one thread that was framed as a product launch announcement, describing a new feature in a way that was only interesting if you already cared about the product, generated zero community engagement and zero citations. The community detected the promotional framing and ignored it. This confirmed that practitioner-framed content is not just a best practice recommendation but a functional requirement.

Day 90 Results

The 90-day measurement across all four tracked AI systems.

Perplexity: 14 of 20 tracked queries now surface the brand. Starting point: zero. The citations covered a range of query types: category queries, comparison queries, and use-case-specific queries. Citation context was positive in 12 of 14 instances, describing the brand as a relevant option for specific use cases. Two citations were neutral list-inclusion mentions without specific context.

ChatGPT (GPT-4 with Browse): 3 of 20 query variations now mention the brand. These are first training-cycle signals, appearing in Browse-enabled responses that retrieve live content. The three citations all traced back to Reddit threads that ranked on Google page one by day 90. Zero citations appeared in non-Browse ChatGPT responses at day 90, which is expected: training data cycles hadn't incorporated the new Reddit content yet. Those signals will compound into non-Browse responses over the following 90 to 180 days.

Google AI Overview: brand appears in 4 query responses. Two pulled from FAQ schema on service pages, two from Reddit threads that had climbed to Google page one. All four were positive, specific citations within category recommendation contexts.

Google organic rankings: 2 Reddit threads now rank on page one of Google for target long-tail queries. 3 additional threads rank on page two. These rankings compound over time as threads continue accumulating engagement.

G2 review count: 8 new reviews added during the engagement, covering 4 distinct use cases. These reviews appeared in 3 Perplexity citations as a direct source, validating that structured third-party review presence contributes independently to citation frequency.

What Drove the Results

Breaking down the 14 Perplexity citations at day 90 by source type: 12 of 14 trace directly to Reddit content, either Reddit threads appearing as cited sources in Perplexity answers, or Reddit threads that ranked on Google page one and were retrieved by Perplexity's upstream search fetch. The remaining 2 cite FAQ schema additions on service pages.

Reddit is the primary driver. Not because other signals don't contribute, but because Reddit content combining high domain authority with peer-written, experience-based specificity is exactly what Perplexity's retrieval layer weights most heavily. A practitioner-written Reddit thread describing a real evaluation experience outcompetes brand content, analyst summaries, and most third-party comparison articles for Perplexity citation frequency.

FAQ schema on service pages contributed independently and faster than expected. The three Google AI Overview appearances that came from service pages rather than Reddit appeared earlier in the timeline than the Reddit-sourced citations, because the service pages had existing Google rankings that the schema additions improved. Structured data is not sufficient on its own, but it accelerates citation appearances for brands that already have some Google presence.

Comparison-format content outperforms question-format for citation extraction. This was the most actionable finding of the engagement. A thread structured as "we evaluated X, Y, and Z for this specific use case, here is what we found" generates Perplexity citations more reliably than a thread structured as "what does everyone think about X?" The comparison format gives the LLM a specific, extractable claim: "practitioners who evaluated these tools found this." The question format generates interesting community discussion but less citable substance.

G2 reviews added third-party validation that reinforced brand context in citations. The reviews contributed two direct Perplexity citations and one indirect Google AI Overview appearance. More importantly, having structured positive reviews on G2 reinforces the brand description AI systems use when they do cite the brand. A brand with 8 G2 reviews gets described more accurately and more specifically than a brand with zero reviews, because the review content gives AI systems concrete use-case language to draw from.

What to Expect From a Similar Engagement

The timeline is real. ChatGPT citations are slower than Perplexity because ChatGPT depends on training cycles that run on their own schedule, not on content publication timing. A brand starting an AEO program today should expect Perplexity results within 60 to 90 days and meaningful ChatGPT results within 150 to 180 days. Trying to shortcut this by posting high volumes of thin content doesn't work: this engagement found that 8 well-structured threads outperformed the leading competitor's 40 thin ones in citation rate, because citation frequency is driven by the quality of the community engagement the thread generates, not by the number of posts.

Measurement is not optional. Without Peec AI tracking, this engagement would have had no way to identify which thread formats drove citations, which queries were moving, or how the brand's share of voice was changing relative to competitors. The weekly probing schedule was what made the month-two format shift possible: the Day 30 data showed comparison threads outperforming question threads, so the month-two plan shifted to comparison-heavy. That data-driven adjustment is only possible if you're measuring.

The channel that matters most is Reddit, but Reddit alone isn't sufficient. FAQ schema contributed. G2 reviews contributed. The combination of Reddit presence (primary), structured data (secondary), and third-party review volume (supporting) produces citation frequency that no single channel generates alone. The Reddit content is the foundation. The other signals are what make the citations accurate, specific, and sustained.

For the full breakdown of how Nerativ approaches AEO engagements from audit through ongoing operations, see the methodology page. For the service specifics, see AEO and LLM citations. For practical guidance on building the Reddit presence that drives these results, see how to get your brand cited in ChatGPT.