How to Get Your SaaS Recommended by ChatGPT in 2026

"How do I get recommended by ChatGPT?" is the new "how do I rank #1 on Google". The short answer: AI assistants build their shortlists from a small, findable set of sources, mostly Reddit threads, listicles and comparison pages, and you can win those sources deliberately. That practice has a name: GEO (generative engine optimization), the work of earning citations and recommendations inside AI answers instead of blue links.

The difference with classic SEO: Google took years and a big content budget. Influencing AI answers is faster, cheaper, and, for now, far less competitive.

Here is the system we use at Readyt, condensed. It works for any SaaS, from side project to Series A.

Step 1: Map the prompts that matter

Before optimizing anything, know what you're optimizing for. List 20-50 prompts your buyers actually type:

• "best [category] for [audience]"
• "[competitor] alternatives"
• "how do I solve [pain point]"
• "[your category] with [key feature]"

Run each through ChatGPT, Claude, Perplexity and Google AI Overviews. Record: who gets named, in what order, and which sources are cited. That last column is your roadmap, it tells you exactly which pages and threads to win. An AI visibility tracker automates the weekly re-runs so you see movement instead of snapshots, but a spreadsheet is fine to start.

Step 2: Which sources does ChatGPT actually cite for SaaS prompts?

In the SaaS prompt sets we track, three surface types keep showing up as cited sources: Reddit threads, listicles and comparison pages, and vendors' own sites. Public citation studies point the same way, for commercial queries, roughly 40% of the sources AI engines cite come from Reddit. Win those three surfaces and you've covered most of the shortlist-building material a model ever sees.

Reddit threads

The single highest-leverage surface. Reddit dominates AI citations, and that's not an accident, the models are contractually plugged into it.

In 2024, Google signed a content-licensing deal with Reddit reported at $60M per year, and OpenAI announced its own Reddit partnership months later. AI answers lean on Reddit because their makers paid for exactly that.

One authentic comment in the right thread can shape answers for months. The workflow: pull the threads cited for your prompts, sort by how often they recur, and earn a place in the top ones, a genuinely useful answer first, your product named only where it honestly fits, following the no-ban playbook. A sustainable cadence beats a burst: a handful of real answers per week from a warmed-up account outlasts ten drive-by comments that get the account flagged.

Listicles and comparison pages

"Top 10 [category] tools" pages get cited constantly. Two moves:

1. Get added to the listicles already being cited. Most authors respond to a short, specific pitch: name their article, point out the omission their readers would care about, and hand them a one-paragraph blurb they can paste as-is. Some will ask for payment or a swap, judge case by case; a placement in a listicle that shows up in your citation tracking is usually worth more than it costs.
2. Publish your own honest comparison pages ("[You] vs [Competitor]"). LLMs cite vendor comparison pages surprisingly often when they're substantive and fair, which means naming the cases where the competitor genuinely wins. The "we win every row" tables read as marketing and get skipped.

Your own site, structured for extraction

Models quote pages they can parse. On your key pages: lead with a one-sentence answer to "what is product] and who is it for", use headings that match real questions, include concrete numbers (pricing, limits, integrations) and named use-cases, and keep the pricing page current, assistants love answering "how much does X cost" and will happily quote a stale number forever. Add an [llms.txt file, a plain-text index of your most important pages for AI crawlers; cheap to do, occasionally decisive. And check your robots.txt: plenty of SaaS sites block GPTBot by default, then wonder why they're invisible in AI answers.

Step 3: Feed the loop with proof

LLMs echo consensus. Consensus is built from mentions. The practical levers, in order of effort-to-impact:

• Answer questions publicly (Reddit, Quora, niche forums, HN when relevant)
• Get reviewed on G2/Capterra, review pages are heavily retrieved for "best X" prompts
• Ship data content, original stats get cited because assistants love citing numbers
• Encourage customers to name you when they write about their stack

Step 4: Measure weekly, not once

AI answers are volatile, models update, sources shift, competitors move. Re-run your prompt set weekly and track three numbers:

1. Visibility rate: % of prompts where you're named at all
2. Average position: where you appear in the shortlist
3. Citation share: % of cited sources you control or appear in

Teams that track weekly spot regressions early, a delisted thread, a competitor's new comparison page, and respond in days, not quarters.

FAQ

Paul-Marie Hamon

Founder @ Readyt

Paul-Marie is the founder of Readyt, the Reddit growth platform for SaaS. He has generated 16K€+ in pre-sales in 2 months using nothing but Reddit, and now helps founders turn Reddit threads into their #1 acquisition channel.