See if ChatGPT and Gemini recommend your SaaS company, or your competitor
A growing share of B2B pipeline starts when buyers ask ChatGPT or Gemini for a vendor shortlist, not a Google results page.
Type in your brand and a primary competitor. See your AI Share of Voice across real B2B software buyer prompts in under 2 minutes.
Measure first, then improve G2 presence, comparison pages, and third-party citations models learn from.
Who controls saas recommendations today?
- Salesforce Sales CloudCategory incumbent
- HubSpot CRMHigh recall
- Zoho CRMHigh recall
Mention share is a directional snapshot from category audits and public research, not a live crawl of every chat. We run the same buyer-style prompts on ChatGPT 5 and Gemini 2.5 Flash-Lite each time so you can compare runs month to month. Leaderboard data (benchmark discovery) last updated Oct 1, 2026; pages re-fetch about once a week.
The prompts your buyers are typing into chat right now
Copy a line into your deck, or run the audit and we ask it on ChatGPT 5 and Gemini 2.5 Flash-Lite.
- ChatGPT 5Gemini 2.5 Flash-Lite
“Best CRM for a 50-person B2B sales team?”
- ChatGPT 5Gemini 2.5 Flash-Lite
“Project management tool for remote engineering teams?”
- ChatGPT 5Gemini 2.5 Flash-Lite
“Payroll software for US startups with contractor support?”
How top brands win AI citations in saas
Winning visibility in LLM chat threads requires a shift from traditional keyword SEO to Answer Engine Optimization (AEO). Models don't rank keywords; they synthesize consensus from a trusted set of foundational surfaces.
- G2
Collect verified reviews in your use-case category
- Capterra
Complete profile and comparison pages
- Reddit r/SaaS
Answer tool recommendation threads
Why AskLLM vs. manual checks
| Factor | AskLLM | Manual tab-swapping |
|---|---|---|
| Time to result | Minutes (guided flow) | Hours across fragmented tabs |
| Models covered | ChatGPT 5 and Gemini 2.5 Flash-Lite | Usually one model at a time |
| Scored output | Standardized visibility index + breakdown | Unstructured notes |
| Repeatability | Same prompt weighting every run | High variance by teammate |
For SaaS, re-run the audit after major releases to see whether models associate your new positioning with the buyer problems you care about.
Stop losing the AI shortlist to category incumbents
Common questions
- Why does AI visibility matter for SaaS more than classic SEO alone?
- Many buyers now shortlist vendors from AI answers. Ranking on Google does not guarantee your brand is recommended in ChatGPT or Gemini. AskLLM measures that gap explicitly.
- Which AI models does the audit test?
- ChatGPT and Gemini - the same models buyers use for product research.
- Is the audit really free?
- Yes. You get a scored report with actionable recommendations. Optional consulting is available if you want hands-on help improving your score.
- How does this audit help with AI search and GEO?
- Traditional SEO targets static engine result pages (SERPs). Generative Engine Optimization (GEO) measures how AI models like ChatGPT and Gemini evaluate your brand's use-case fit, integration depth, and analyst-style review corroboration to build their conversational recommendations.
- What should I do if my brand never shows up for saas questions in ChatGPT or Gemini?
- Start by matching how buyers actually ask (see the prompt examples above). Put that language on your product pages, then earn mentions on the independent sites listed in this category. Add or refresh third-party reviews, publish testing or COA details where shoppers look, and run an audit with one real competitor so you can see which prompts you lose. Re-run after those changes; movement on the same prompt pack is what matters.
- How often should we re-run the audit?
- Re-audit after anything models might notice: a new SKU, retailer launch, clinical or third-party test, major PR, or a site migration. For reporting, many teams run monthly or quarterly on the same competitor and category so the score is comparable. The public leaderboard on this page refreshes about once a week from benchmark and audit aggregates.