Key takeaways
- Most AI visibility platforms look similar on a feature matrix — the real differences show up one layer deeper, in data collection methods, content capabilities, and workflow fit.
- Monitoring is table stakes. The question that actually matters is: does the platform help you fix what it finds?
- Ask about specific model versions, query frequency, crawler log access, and traffic attribution before signing anything.
- Platforms that can't show you prompt volume data, content gap analysis, or citation-level detail are tracking-only tools — useful for reporting, not for growth.
- The 25 questions below are organized into seven evaluation areas. Run any vendor through them and the right choice usually becomes obvious within the first three sections.
AI search visibility is no longer a niche concern. ChatGPT processes roughly 2 billion queries daily. Google AI Overviews reaches 2 billion monthly users. AI referral traffic grew 527% year-over-year in 2025, according to Digiday. Yet only 22% of marketers are actively tracking their AI visibility, per Position Digital's research.
That gap is closing fast, and a new category of platforms has emerged to help brands close it. The problem: they all look roughly the same on a feature comparison page. Every vendor tracks brand mentions across LLMs. Every vendor has a dashboard. Every vendor claims to cover "all major models."
The features overlap by about 80%. The differences that actually matter sit one layer deeper.
This checklist gives you 25 specific questions to ask before you commit to a contract. They're organized into seven evaluation areas. Some questions are designed to catch vague answers. Others are designed to surface capabilities that most vendors don't volunteer unless you ask directly.
Section 1: Model coverage (questions 1-4)
This is where most vendor conversations start, and where the most misleading answers get given.
Question 1: Which specific models do you monitor, and which version of each?
"All major LLMs" is not an answer. Push for a list: ChatGPT (which version -- 4o, o3, o4-mini?), Claude (Opus, Sonnet, Haiku?), Perplexity, Google AI Overviews, Google AI Mode, Gemini, Grok, DeepSeek, Copilot, Meta AI. The version matters because different model versions produce meaningfully different citation behavior. A platform that monitors ChatGPT 4o but not o3 is missing a significant share of real user queries.
Question 2: Do you query models through their user-facing interfaces or through APIs?
This is one of the most important questions on this list, and most buyers never ask it. API outputs and user-facing answers can differ substantially -- especially for shopping recommendations, citations, and featured sources. A platform that only hits the API may be showing you data that doesn't reflect what actual users see.
Question 3: How frequently do you query each model?
Daily? Weekly? On-demand? For some use cases, weekly snapshots are fine. For competitive monitoring or campaign tracking, you need daily or near-real-time data. Ask specifically -- don't accept "regular cadence" as an answer.
Question 4: What geographic and language coverage do you support?
If your customers are in Germany, France, or Japan, you need to know whether the platform queries models in those languages and from those regions. AI models can return different answers depending on the user's location and language. Many platforms support English only, or charge extra for additional regions.
Section 2: Prompt and query intelligence (questions 5-8)
Tracking brand mentions is only useful if you're tracking the right prompts. This section separates platforms with real prompt intelligence from those that just let you type in a few keywords.
Question 5: Do you provide prompt volume estimates and difficulty scores?
Knowing that you're invisible for a prompt is one thing. Knowing whether that prompt gets asked 50 times a month or 50,000 times is what lets you prioritize. Ask whether the platform provides volume estimates and difficulty scores for each prompt, so you can focus on high-value, winnable queries instead of guessing.
Question 6: Do you support query fan-outs?
A single user prompt often branches into multiple sub-queries that an AI model uses to construct its answer. Platforms that understand fan-outs give you a much more complete picture of how AI engines actually process questions about your category. This is a relatively advanced capability -- most basic monitoring tools don't have it.
Question 7: Can I define custom prompts, or am I limited to a fixed prompt library?
Some platforms (notably Semrush's AI tracking features and Ahrefs Brand Radar) use fixed prompt sets. That means you're tracking visibility for questions the vendor chose, not the questions your actual customers ask. Custom prompt definition is a baseline requirement for any serious use case.
Question 8: How many prompts can I track at my price tier?
This question reveals a lot about the platform's pricing model. Some tools charge per prompt, which gets expensive fast. Others bundle prompts into tiers. Know the number before you sign -- and think about how many prompts you'd realistically need to cover your category properly.
Section 3: Content gap analysis and optimization (questions 9-13)
This is the section that separates monitoring tools from optimization platforms. Most vendors stop at showing you data. A smaller number help you act on it.
Question 9: Does the platform show me which prompts my competitors rank for that I don't?
Answer gap analysis -- seeing exactly where competitors are visible and you're not -- is the most actionable output an AI visibility platform can produce. It tells you specifically what content your site is missing. If a vendor can't show you this, they're a monitoring dashboard, not an optimization tool.
Question 10: Does the platform generate content to fill those gaps?
Identifying a gap is step one. Generating content to fill it is step two. Ask whether the platform has built-in content generation capabilities -- and if so, whether that content is grounded in real prompt data, citation analysis, and competitor research, or whether it's just generic AI writing. There's a meaningful difference between content engineered to answer specific AI gaps and content that happens to be on-topic.
Question 11: Can I provide brand guidelines, tone of voice, and uploaded knowledge base files to the content generation system?
Generic AI content doesn't win citations. Content that reflects your brand's actual expertise and voice has a much better chance. Ask whether the content system accepts brand instructions, uploaded documents, and custom knowledge bases.
Question 12: Does the platform track results after I publish new content?
The full optimization loop is: find gaps, create content, track whether it gets cited. If a platform can't close that loop -- if it can't show you whether your new article moved the needle on AI citations -- you're flying blind on whether your work is actually paying off.
Question 13: How long does it typically take from content publication to first AI citation?
This is partly a question about the platform's tracking granularity and partly a reality check on expectations. Good platforms can show you the timeline from publish to crawl to citation. If a vendor can't answer this, they probably don't have the crawler-level data to know.
Section 4: Citation and source analysis (questions 14-16)
Understanding which sources AI models cite -- and why -- is essential for knowing where to invest your content and PR efforts.
Question 14: Can I see exactly which pages, domains, Reddit threads, and YouTube videos AI models are citing in their responses?
Citation-level detail tells you where AI models are actually going for information. If Reddit threads and YouTube videos are driving citations in your category, you need to know that -- and you need to be building a presence there. Many platforms show you aggregate brand mention scores without revealing the underlying source data.
Question 15: Do you track offsite citations -- mentions on third-party sites, listicles, and review platforms?
Your AI visibility isn't just determined by your own website. Third-party mentions, comparison articles, and community discussions all influence what AI models recommend. Ask whether the platform tracks these offsite signals, not just your own domain.
Question 16: Do you track ChatGPT Shopping recommendations and entity mentions separately?
ChatGPT's shopping and product recommendation features operate differently from its general search behavior. If you sell products, this is a distinct channel worth tracking separately. Most platforms don't break this out.
Section 5: Crawler logs and technical visibility (questions 17-19)
This section is often overlooked by buyers who focus on the front-end dashboard. It's where some of the most actionable data lives.
Question 17: Do you provide AI crawler logs -- real-time data on which AI bots are hitting my site, which pages they're reading, and what errors they encounter?
AI crawler logs show you how AI engines discover and index your content. If GPTBot is hitting your site but encountering JavaScript rendering errors, your content may never make it into ChatGPT's training or retrieval pipeline. This is technical data that most monitoring-only platforms don't have at all.
Question 18: How do you integrate with my website to get this data?
Common integration methods include Cloudflare or Fastly workers, Vercel integrations, server log parsing, Google Search Console connections, or a lightweight tracking snippet. Ask which methods are available and what the setup time looks like. Some integrations are genuinely low-latency and non-invasive; others require significant engineering work.
Question 19: Can I see which of my pages are being cited and how often, at the page level?
Aggregate brand visibility scores are useful for reporting. Page-level citation tracking is useful for optimization. You need to know whether it's your homepage, your comparison pages, or your blog posts that are actually getting cited -- so you can double down on what's working.
Section 6: Reporting, integrations, and team fit (questions 20-23)
Even a technically excellent platform fails if it doesn't fit how your team actually works.
Question 20: What does setup time look like from signup to first useful insight?
Some platforms are genuinely self-serve and return data within hours. Others require onboarding calls, custom configuration, and a week or more before you see anything useful. Ask for a realistic timeline, and ask whether there's a free trial that lets you verify the data quality before committing.
Question 21: Does the platform integrate with our existing reporting stack?
Ask specifically about Looker Studio, Google Data Studio, Slack, and whether there's an API for custom workflows. If your team lives in Looker Studio for reporting, a platform that doesn't connect to it creates extra work every week.
Question 22: Who is this platform designed for -- analysts, marketers, founders, or agencies?
The answer shapes everything from the UI to the default metrics to the support model. A platform built for enterprise analysts will frustrate a lean marketing team. A platform built for solo founders may not have the multi-seat, multi-brand features an agency needs.
Question 23: Do you support multi-language and multi-region monitoring with customizable personas?
If your customers prompt AI models in different languages or from different countries, you need the platform to reflect that. Persona customization -- setting the geographic location, language, and user type for each query -- is important for getting data that matches your actual customer behavior.
Section 7: Pricing, contracts, and data integrity (questions 24-25)
The last two questions are about protecting yourself before you sign.
Question 24: How is pricing structured, and what happens when I exceed my limits?
Understand exactly what you're paying for: number of sites, prompts per month, content articles generated, seats, and regions. Ask what happens if you go over -- do you get charged automatically, or does the platform pause tracking? Overage charges can make a "cheap" plan expensive fast.
Question 25: How do you ensure data accuracy -- are you actually querying live AI models, or using cached or synthetic data?
Some platforms query AI models in real time. Others use cached responses or synthetic data to reduce costs. The difference matters because AI model behavior changes frequently -- a cached response from two weeks ago may not reflect what the model says today. Ask directly how fresh the data is and how often it's refreshed.
How the major platforms stack up
Running these questions across the major platforms in 2026 produces a fairly clear picture of which tools are built for monitoring and which are built for optimization.
| Platform | Custom prompts | Content generation | Crawler logs | Offsite citations | ChatGPT Shopping | Multi-region | Traffic attribution |
|---|---|---|---|---|---|---|---|
| Promptwatch | Yes | Yes (Content Agents) | Yes | Yes | Yes | Yes | Yes |
| Profound | Yes | No | No | Limited | No | Limited | No |
| AthenaHQ | Yes | No | No | No | No | Yes | No |
| Otterly.AI | Yes | No | No | No | No | Limited | No |
| Peec.ai | Yes | No | No | No | No | Limited | No |
| Semrush | Fixed | No | No | No | No | Limited | No |
| Ahrefs Brand Radar | Fixed | No | No | No | No | No | No |
| ScrunchAI | Yes | No | No | No | No | Limited | No |
The pattern is consistent: most platforms in this category are monitoring dashboards. They show you where you're invisible. They don't help you become visible.
Promptwatch is the clearest exception -- it's built around what it calls an "action loop": find gaps with Answer Gap Analysis, generate content with Content Agents, then track results at the page level with crawler logs and traffic attribution. It's the only platform in the comparison above that covers all seven columns.
For teams that just need a basic monitoring signal, tools like Otterly.AI or Peec.ai are simpler and cheaper starting points.
Otterly.AI
For enterprise teams with existing SEO infrastructure who want AI visibility bolted onto a familiar platform, Profound covers the monitoring side well.
Profound
For teams that want traditional SEO and AI visibility in one place, Semrush has added AI tracking features -- but the fixed prompt library is a real limitation if you need to track category-specific queries.
A note on what to do with the answers
The point of this checklist isn't to find a platform that answers "yes" to all 25 questions. It's to understand what you're actually buying.
If you're a small team that needs a basic signal -- "are we showing up in AI answers for our core queries?" -- a monitoring-only tool at $50-100/month is probably fine. You don't need crawler logs or content generation yet.
If you're a marketing team that wants to actively improve AI visibility and tie it to revenue, you need a platform that closes the full loop: gap analysis, content generation, page-level tracking, and traffic attribution. That's a different product category, and the price reflects it.
The worst outcome is paying for an optimization platform when you only use the monitoring features, or paying for a monitoring tool when you actually need to be creating content and tracking results.
Use these 25 questions to figure out which category you're in -- and then pick the platform that was actually built for it.