Most AEO tools only check ChatGPT. That misses over half the picture. We score AI visibility across 6 engines — ChatGPT, Perplexity, Gemini, DeepSeek, Claude, and Mistral — because a tool that scores 3/3 on ChatGPT can score 1/3 on DeepSeek. If you only measure one engine, you don't know where you're invisible.
The Problem With Single-Engine AEO
When SaaS founders first hear about AEO (AI Engine Optimization), the natural instinct is to focus on ChatGPT. It's the most popular AI assistant, so it gets the most attention. But here's what we found after running hundreds of audits: ChatGPT visibility tells you almost nothing about your visibility on other engines.
Each AI engine uses different training data, different retrieval methods, and different ranking signals. Perplexity searches the live web and cites sources. Gemini pulls from Google's Knowledge Graph. DeepSeek draws heavily from Chinese and English technical content. Claude emphasizes safety and factual accuracy. Mistral has its own European-trained model.
A SaaS tool can be confidently recommended by ChatGPT and completely unknown to DeepSeek. We've seen this happen with well-known products, not just obscure startups.
Why 6 Engines?
We chose these 6 engines because they represent the major AI assistants that real users rely on for product recommendations:
- ChatGPT (OpenAI) — The market leader with the largest user base. Uses GPT-4o for most queries.
- Perplexity — The AI search engine that cites real-time sources. Growing rapidly among researchers and professionals.
- Gemini (Google) — Integrated into Google Search via AI Overviews. Reaches the broadest audience through Google's ecosystem.
- DeepSeek — The Chinese AI that has gained global traction for technical queries. Strong in developer and engineering communities.
- Claude (Anthropic) — Known for careful, nuanced responses. Popular among enterprise users and developers.
- Mistral — European AI model with strong multilingual capabilities. Growing in EU markets.
Together, these 6 engines cover the vast majority of AI-assisted product discovery. If your tool is visible across all 6, you're reachable by virtually every AI user. If you're only visible on 1-2, you're leaving most of the market on the table.
How the Scoring Works
Each engine receives the same query: 'What is [your tool] and what does it do?' The response is analyzed for three signals:
- Not mentioned (1 point) — The engine doesn't know your product exists.
- Mentioned but inaccurate (2 points) — The engine mentions your product but with incomplete or incorrect information.
- Cited accurately (3 points) — The engine describes your product correctly, including features, use case, and category.
Your total AEO score ranges from 6 (invisible on all engines) to 18 (accurately cited on all 6). We classify visibility into four levels: Critical (6–8), Low (9–12), Moderate (13–14), and High (15–18).
Real Data: How Scores Vary Across Engines
Here's what we see in practice when we audit well-known SaaS tools. Even established products show significant variation:
Notion scores 18/18 — all 6 engines cite it accurately. But most SaaS tools aren't Notion. A typical B2B SaaS product scores 10–13/18 on first audit, with 2–3 engines returning incomplete or missing information.
The most common pattern: strong ChatGPT + Perplexity scores (because these pull from well-indexed English web content) but weaker DeepSeek and Mistral scores (which rely more on their own training data). This is the 'AI visibility gap' — the spread between your best and worst engine scores.
What Drives Differences Between Engines
Through our audits, we've identified several factors that explain why the same product gets different scores on different engines:
- Training data recency — Engines with newer training cutoffs are more likely to know about recently launched tools.
- Retrieval augmentation — Perplexity and Gemini search the live web; ChatGPT and Claude rely more on parametric knowledge. Fresh content helps more with retrieval-augmented engines.
- Structured data parsing — Engines that parse JSON-LD and schema markup can extract accurate product information even if the brand isn't well-known.
- Language and regional bias — DeepSeek performs better on tools with Chinese-language presence. Mistral weights European sources more heavily.
- Category crowding — In competitive categories (CRM, project management), smaller tools get drowned out. In niche categories, even lesser-known tools can score 3/3 on most engines.
What You Can Do About It
The good news: improving your AEO score across all 6 engines uses the same core principles. You don't need 6 different strategies. You need one strategy executed well:
- Use answer-first formatting — pages cited by AI engines tend to put the core answer in the first 40-60 words. AI models extract concise answers from early text. Magnitude is qualitative across our 4 published audit teardowns; we have not run a controlled study.
- Add structured data (JSON-LD) — Product schema, FAQ schema, and Organization schema help all engines parse your information accurately.
- Keep content fresh — recently-updated pages are preferentially cited per Google's freshness documentation. The exact decay rate is not publicly documented. Maintain visible last-updated dates on key pages.
- Be entity-consistent — Use the same product name, description, and category across your site and third-party profiles.
- Build citation-ready snippets — Include quotable stats, clear feature lists, and direct comparisons that AI engines can extract.
Run Your Free 6-Engine Audit
Want to see how your SaaS tool scores across all 6 AI engines? Our free AEO audit queries ChatGPT, Perplexity, Gemini, DeepSeek, Claude, and Mistral in real time and generates a visibility report with your score out of 18. No signup required.
Try it now at eurekanav.com/aeo/free-audit — it takes 30 seconds and you'll see exactly where you're visible and where you're invisible.
Sources & references
Each external claim in this post links to a primary source. Where we cite our own observations, we disclose sample size (currently n=4 published audit teardowns plus broader audit work). For methodology details and our 6-engine scoring approach, see eurekanav.com/methodology.
If you spot a claim in this post that you cannot trace to a source above or to our methodology, email don@eurekanav.com — we will provide one or correct the claim within 24 hours.
