TL;DR — Read Visibility > Responses filtered to evaluation prompts to see the exact pros, cons, and pricing AI associates with your software. Go to Strategy > Brand Perception for an aggregated SWOT showing which strengths and weaknesses appear across multiple AI models. Check Insights > Shopping Results to verify whether AI quotes your current pricing accurately. Pro tip: export the Brand Perception SWOT and compare it to your internal positioning doc — every mismatch is a content opportunity to correct outdated AI narratives.
The Question
“What do AI models say about my software’s pros, cons, and pricing?”When a potential customer asks an AI “what are the pros and cons of [your software]” or “is [your software] worth the price”, AI generates an evaluation that may reach thousands of similar prospects. This evaluation is not a random output — it is synthesized from crawled reviews, your own documentation, comparison articles, and community discussions. Understanding exactly what AI says, and why, is the foundation of any GEO improvement strategy for software products. You might also be wondering:
- “Is the pricing AI mentions for my software accurate and up to date?”
- “What limitations does AI associate with my product, and are they fixable?”
- “How do AI evaluations of my software compare to what competitors’ evaluations say?”
Where to Go in Qwairy
Start here: Visibility > Responses (deep analysis, read verbatim)
Navigate to Visibility > Responses — your primary view for reading exactly what AI says about your software.
Filter to prompts that ask for evaluations, pros and cons, reviews, or pricing. Read 5-10 responses across different providers.
Note the exact language AI uses: which pros appear consistently, which cons are mentioned repeatedly, whether pricing is described accurately, and whether the framing is positive, neutral, or cautionary.
Go deeper: Strategy > Brand Perception (SWOT) + Sentiment Analysis
Navigate to Strategy > Brand Perception to see a structured SWOT-style analysis derived from AI response data across your monitored prompt set.
This view aggregates the themes from Responses into a synthesized view: what strengths AI consistently attributes to your software, what weaknesses it mentions, what opportunities it identifies, and what threats or competitive risks it associates with your category.
Then open Insights > Sentiment Analysis to understand the emotional valence of your software’s mentions — positive, neutral, or negative — broken down by provider and topic.
Complete the picture: Insights > Shopping Results (pricing) + MCP
Navigate to Insights > Shopping Results to verify the specific pricing information AI associates with your software plans.
This view surfaces whether AI quotes your current prices, outdated prices, or conflates your pricing tiers with a competitor’s.
Use the MCP integration to query AI models about your software directly and compare real-time responses to the historical data in Qwairy — this reveals whether recent content updates have changed how AI represents your product.
What to Look For
Responses — Verbatim Software Evaluation Text
The Responses view is the most direct window into your AI reputation. Software evaluation queries (“pros and cons of X”, “is X worth it”, “X honest review”) produce AI outputs that your prospects read and trust. Unlike marketing copy, AI evaluations are perceived as neutral — which makes negative or inaccurate framing particularly damaging.| Element | What it tells you |
|---|---|
| Consistently mentioned pros | The strengths AI has extracted from your content, reviews, and third-party sources — validate these match your intended positioning |
| Consistently mentioned cons | The limitations AI repeatedly associates with your software — identify whether these are real, outdated, or sourced from stale reviews |
| Pricing mentions | Whether AI quotes specific prices and whether those prices are accurate — inaccurate pricing erodes trust before the first call |
| Comparison framing | Whether AI positions your software as the premium option, the value option, or the “it depends” option |
| Caveats and qualifiers | Phrases like “not suitable for enterprise”, “limited support”, “steep learning curve” — these are the AI reputation risks to address |
Brand Perception — Aggregated SWOT Analysis
Where Responses shows you individual data points, Brand Perception aggregates them into patterns. A single AI response mentioning “limited reporting” is noise; five different AI models consistently mentioning “limited reporting” across evaluation prompts is signal that requires action.Pro Tip: Export the Brand Perception SWOT data and compare it to your internal product positioning document. Every gap between how AI describes your software and how you want it described is a content opportunity: either your product page does not emphasize that attribute, or a stale review from an older version of your product is shaping the AI’s view.
Filters That Help
| Filter | How to use it for this question |
|---|---|
| Provider | Different AI models often form different opinions about the same software — identify which model is most favorable and which is most critical |
| Period | Use “last 30 days” after a major product update or pricing change to verify the new information has propagated into AI responses |
| Topic / Tag | Filter to specific evaluation tags (pricing, support, integrations, ease of use) to analyze each dimension of your software’s AI representation separately |
How to Interpret the Results
Good result
AI responses across multiple providers consistently describe your software with accurate pros that match your intended positioning, mention pricing that is within 5-10% of your actual current prices, and either omit the cons or frame them as minor and category-standard trade-offs. Brand Perception shows a strong Strengths section with limited Weaknesses, and Sentiment Analysis reads predominantly positive (above 60% positive mentions).Needs attention
AI consistently mentioning a con that is no longer accurate (a limitation fixed in a major release 12 months ago), quoting a pricing tier that no longer exists, or describing your software with generic attributes rather than your specific differentiators — indicating AI has low-quality or outdated source material about your product. Also watch for Sentiment Analysis showing above 15% negative mentions, which suggests critical reviews or community discussions are influencing AI evaluations.Example
Scenario: Your project management software released a major analytics module 8 months ago, but AI still consistently cites “limited reporting features” as a con in every evaluation response.
- Open Visibility > Responses and filter to evaluation prompts. Read 6 responses across ChatGPT, Perplexity, and Gemini. All six mention “limited reporting compared to competitors” despite your analytics module being a top-reviewed feature in recent user reviews. The responses cite a Capterra review from 14 months ago and a TechRadar roundup from 18 months ago.
- Navigate to Strategy > Brand Perception — the Weaknesses quadrant shows “reporting and analytics” appears in 68% of AI evaluation responses. The Strengths quadrant correctly shows “task management” and “collaboration features” but does not include analytics.
- Check Insights > Shopping Results — pricing is accurate. The issue is purely reputational, not commercial. Execute a three-part response: update your Capterra listing to highlight the new analytics module with screenshots, submit a product update post to Product Hunt, and pitch a “before/after analytics” case study to 2 SaaS media publications that AI cites in your category. Set a 60-day review period in Qwairy to measure whether the “limited reporting” con frequency in Responses decreases.
Go Further
How AI extracts pros/cons
Read the Brand Perception documentation to understand how AI models extract and present your product’s pros, cons, and pricing
Pros/cons tracking over time
Track how AI-generated pros, cons, and pricing mentions evolve over time using the answer-details data source
Detect pricing mentions via API
Use the answers endpoint to programmatically detect and monitor pricing mentions across AI providers
Related Questions
How is my brand perceived by AI models overall?
Broader brand perception analysis beyond software-specific pros and cons
Are my products showing up in AI shopping recommendations?
The commerce angle — whether AI recommends your software in purchase-intent queries
What is the sentiment of AI mentions of my brand?
Deep dive into the positive, neutral, and negative sentiment breakdown by provider and topic