How Does ChatGPT Decide Which Software To Recommend?

Somewhere right now, a SaaS founder just asked ChatGPT “what’s the best project management tool for a remote team?” and got a list of five recommendations. Their product wasn’t on it. They’ll spend the next week wondering why — and almost certainly blaming the wrong things. Maybe they’ll tweak their homepage copy. Maybe they’ll publish another blog post nobody reads. Maybe they’ll do what every founder does in a crisis: schedule a meeting about it.

Here’s the thing. ChatGPT’s recommendation engine isn’t actually a black box. It’s complex, yes. Probabilistic, absolutely. But the signals it uses to decide which software to recommend are observable, testable, and — most importantly — influenceable. Not through manipulation. Through the same thing that has always earned trust: being genuinely worth recommending.

This post breaks down what we understand about how ChatGPT and similar AI engines select their software recommendations, based on extensive testing through our sister company Qvery. This isn’t speculation. It’s pattern recognition from running thousands of queries across dozens of SaaS categories every single day.

ChatGPT synthesizes recommendations from what the internet collectively says. The brands it recommends are the ones the internet recommends.

The Two-Layer Architecture: Training Data + Real-Time Retrieval

ChatGPT’s recommendations come from two distinct sources. Understanding the difference is critical for any SaaS brand trying to influence its visibility — and most marketers get this wrong because they treat ChatGPT like a search engine with a text box. It’s not. It’s a language model with a research assistant bolted on.

Layer 1: The training data (parametric knowledge)

ChatGPT was trained on a massive corpus of text data — web pages, books, articles, forums, documentation. Everything the model “knows” about software is baked into its parameters during training. When you ask it about well-established brands like Salesforce, Notion, or Slack, it draws heavily on this training knowledge.

The implications are significant:

• The training data has a cutoff date — ChatGPT’s baseline knowledge about your product is frozen at whatever the internet said about you at that point
• If your product was relatively unknown when the model was last trained, your parametric knowledge footprint is thin
• No amount of real-time content creation changes your training data presence — that only updates when the model is retrained

This is why established brands with years of web presence have a structural advantage. They’ve been discussed, reviewed, and referenced across thousands of sources for years. That discussion is embedded in the model’s parameters. Newer brands need to compensate with stronger real-time retrieval signals.

Layer 2: Real-time retrieval (RAG)

Modern ChatGPT doesn’t rely solely on training data. It uses Retrieval-Augmented Generation (RAG) — searching the live web to supplement its knowledge before generating a response.

Think of it like a consultant who studied your industry ten years ago but Googles the latest news before every meeting. The studying is parametric knowledge. The Googling is RAG.

When you ask about software recommendations, ChatGPT searches for current information, retrieves relevant pages, and synthesizes its response from both its training knowledge and the freshly retrieved content. Crucially, the retrieval layer works at the passage level — it doesn’t just find pages, it extracts specific expert passages that answer the query. A single paragraph explaining why your CRM handles pipeline reporting better than competitors can get cited directly in ChatGPT’s response.

This is where the game gets actionable. The sources ChatGPT retrieves in real-time are the sources you can directly influence. If your brand appears on the pages that ChatGPT retrieves — review sites, comparison articles, Reddit discussions, expert roundups — you increase your probability of being included in the recommendation.

Vlad Shvets

CEO @ Empact Partners

AI recommendations are probabilistic, not deterministic. The same query can return different brands on different days depending on retrieval context. The brands that appear most consistently have the strongest training data presence and the most real-time retrievable content across the widest range of independent sources.

The Six Signals ChatGPT Uses To Pick Software

Based on our analysis of thousands of AI search responses across dozens of SaaS categories, six signals consistently predict which brands ChatGPT recommends. Not every signal matters equally for every query, but the pattern is clear. These are signals we’ve independently validated through Qvery’s daily monitoring.

1. Source consensus

ChatGPT looks for agreement across multiple independent sources. If G2 reviews, Reddit discussions, a TechCrunch article, and three comparison blog posts all recommend the same CRM, ChatGPT treats that consensus as a strong signal.

A brand that only one source mentions? Weak candidate. A brand that ten independent sources recommend? Strong one.

This is fundamentally different from Google’s link-based authority model. Google cares about who links to you. ChatGPT cares about how many independent sources say similar things about you. The distinction matters: you can have zero backlinks from a Reddit thread and still benefit enormously from the discussion happening there.

2. Specificity of mention

Vague mentions carry less weight than specific ones. Compare these two:

• Weak signal: “Notion is a popular productivity tool”
• Strong signal: “Notion’s database views make it ideal for managing content calendars across remote teams”

ChatGPT favors sources that explain why a product is good for a specific use case, because that specificity helps it match the right recommendation to the right query. This is passage-level retrieval in action — the AI pulls specific expert passages, not entire pages.

Product tutorials and detailed reviews are gold for AI search visibility. They contain the kind of specific, use-case-matched descriptions that ChatGPT needs to make confident recommendations. (Your homepage saying “the all-in-one platform for modern teams” helps exactly no one.)

3. Source authority

Not all sources are equal. ChatGPT weighs some more heavily than others:

• High authority: G2, Capterra, TechCrunch, official documentation, active Reddit communities
• Medium authority: Industry blogs, niche publications, YouTube reviews
• Low authority: Random blog posts, thin affiliate sites, AI-generated listicles

The authority signal isn’t purely about domain authority in the traditional sense. It’s about source type. A genuine user review on G2 from a verified buyer carries different weight than a sponsored blog post, even if the blog post is on a higher-DR domain. ChatGPT can tell the difference between organic discussion and manufactured content. (Turns out the AI read all those “Top 10 Best Tools” affiliate posts too — and learned to ignore them.)

Shanal Govender

Senior GTM Consultant @ Empact Partners

The AI needs to see your product name consistently associated with your category across independent sources — not just your own website. Review platforms, comparison articles, Reddit discussions, industry roundups. Without that distributed presence, you’re invisible to the retrieval layer, no matter how good your product actually is.

4. Recency

For actively evolving product categories, ChatGPT weights recent content more heavily. A 2025 review that says “Figma recently added AI features” matters more than a 2021 review that pre-dates those features. The real-time retrieval layer specifically favors current content.

This creates an opportunity for active brands. If you’re consistently producing fresh content — blog posts, product updates, community engagement — the retrieval layer has current material to work with. Brands that stopped publishing two years ago are relying entirely on their training data footprint, which depreciates over time.

5. Sentiment distribution

ChatGPT doesn’t just count mentions — it reads them. A product with 50 positive mentions and 5 negative ones will be recommended differently than a product with 50 positive mentions and 25 negative ones.

This means genuine product quality matters. You can’t outmarket a bad product in AI search. If your G2 reviews are mediocre, your Reddit sentiment is mixed, and your comparison articles note significant limitations, ChatGPT will incorporate that — often explicitly. “Product X is good for Y but users report issues with Z” is a common pattern in AI responses.

There’s no amount of content marketing that fixes a two-star G2 rating. The best AI search strategy starts with building something people actually want to recommend.

6. Entity recognition

ChatGPT needs to recognize your brand as a distinct entity in the relevant category. This recognition comes from:

• Consistent naming across all platforms
• Structured data (schema markup) on your website
• Presence on entity-establishing platforms — Wikipedia, Crunchbase, LinkedIn
• The cumulative effect of being discussed in context across many sources

Brands with weak entity signals get confused or omitted. If your product name is a common word (like “Notion” or “Stripe”), entity recognition helps the AI distinguish between the software brand and the dictionary meaning. This is the kind of thing that sounds trivial until you realize ChatGPT just recommended a stationery brand instead of your B2B SaaS platform.

Source diversity beats source volume. A brand mentioned across five source types beats one mentioned five times on the same type.

The Probabilistic Nature Of AI Recommendations

Here’s something that trips up a lot of marketers: ChatGPT’s recommendations aren’t deterministic. Ask the same question ten times and you’ll get slightly different answers each time. The same brand might appear in 7 out of 10 responses, or be the top recommendation in 3 and absent in 2.

This is like asking ten different industry analysts for their top CRM pick. You’d get overlapping but slightly different lists each time. ChatGPT works the same way — except it’s one model sampling from a probability distribution instead of ten humans sampling from their experience.

This probabilistic nature is why single-query testing is misleading. Asking ChatGPT “do you recommend our product?” once and seeing a positive response doesn’t mean you’ve won AI search. You need statistical significance — hundreds or thousands of queries across many phrasings — to understand your true visibility.

That’s exactly what Qvery provides. By running queries daily and tracking responses over time, it turns the probabilistic noise into actionable signal. You don’t see one recommendation. You see your recommendation frequency, your average position, and your share of voice against competitors — all based on statistically meaningful sample sizes.

Leon Claassen

Senior GTM Consultant @ Empact Partners

You can’t outmarket a bad product in AI search. If users are posting negative experiences on G2 or sharing frustrations on Reddit, the AI reads all of it and factors it into recommendations. This is the great equalizer — no amount of SEO or content marketing can override a consensus of negative user sentiment across multiple platforms.

What Google AI Mode Does Differently

Google AI Mode operates on similar principles but with one crucial difference: it has access to Google’s search index. While ChatGPT relies on its training data plus real-time retrieval, Google AI Mode taps into the full Google index, PageRank signals, and the entire ecosystem of ranking signals that Google has spent 25 years building.

In practice, this means Google AI Mode’s recommendations skew more toward traditionally well-ranked websites. A brand that ranks well in Google’s organic results is more likely to appear in Google AI Mode responses than in ChatGPT responses.

Conversely, ChatGPT sometimes surfaces smaller brands that have strong community discussion — Reddit, forums, niche communities — but weaker traditional search presence. It processes intent differently than Google, understanding conversational context rather than matching query strings to indexed pages.

For SaaS brands, this means you need to optimize for both:

• Google AI Mode: Traditional search fundamentals still matter — site authority, content quality, technical health
• ChatGPT: Mention footprint and community presence matter more — reviews, Reddit, expert discussions
• Both: High-quality content, strong entity signals, diverse source mentions — this overlap is where the biggest impact happens

We explored the differences between these two engines in depth in our piece on ChatGPT vs Google AI Mode.

Teddy Cipolla

Senior GTM Consultant @ Empact Partners

Strong Google rankings don’t guarantee ChatGPT visibility, and vice versa. We’ve seen brands that rank #1 on Google for their primary keyword but never appear in ChatGPT recommendations — and brands with modest Google rankings that ChatGPT recommends consistently. The retrieval mechanisms are fundamentally different, and your strategy needs to account for both.

How To Influence The Outcome

Understanding the signals is the first step. Influencing them is the second. Here’s how each signal maps to concrete actions.

The common thread across all six signals is the same principle we keep returning to: be genuinely worth recommending. Build a great product. Get real people to talk about it. Create content that helps users solve specific problems.

The AI is remarkably good at separating signal from noise — which means the best “AI search optimization strategy” is to be the signal, not the noise.

If you want help understanding why ChatGPT is or isn’t recommending your product, and what to do about it, that’s exactly the kind of problem we solve at Empact Partners. We use Qvery to diagnose the issue and our GTM playbook to fix it. Let’s look at your AI search data together.

‍