Key takeaways
- Most AI visibility platforms return basic mention counts and citation URLs, but the real value is in the metadata: sentiment scores, source types, competitor comparisons, and page-level attribution
- Platforms like Promptwatch, Profound, and ZipTie expose the richest data schemas with 30+ fields per response, while budget tools like Otterly.AI and Peec AI cap out at 10-15 fields
- If you're building custom dashboards or feeding data into your analytics stack, API export quality matters more than the UI -- look for JSON exports, webhook support, and granular filtering
- For teams that need to prove ROI, the difference between "your brand was mentioned" and "your brand was mentioned in position 2, with positive sentiment, citing your pricing page, beating Competitor X" is the difference between a vanity metric and a business case
You signed up for an AI visibility platform. You connected your brand. The dashboard lights up with mentions. Great. Now what?
The problem isn't that these tools don't track AI search results -- most of them do that fine. The problem is what they give you back. Some platforms hand you a CSV with three columns: prompt, mention (yes/no), date. Others return a 50-field JSON object with sentiment analysis, competitor positioning, citation metadata, and traffic attribution. The difference between those two outputs is the difference between "we got mentioned" and "we can actually optimize this."
I've spent the last six months working with B2B teams on their GEO strategies, and the question I hear most often isn't "which tool should I use?" It's "why can't I export the data I actually need?" So let's fix that. This guide breaks down what each major AI visibility platform returns in its API, which fields matter, and which platforms are worth your time if you're building real optimization workflows.
What makes a good AI visibility data schema
Before we compare platforms, let's define what "useful" means. A good data schema for AI visibility tracking should give you:
Core mention data
- Prompt text: The exact query that triggered the response
- Response text: The full AI-generated answer (not just a snippet)
- Brand mention: Whether your brand appeared, and where (position 1, 2, 3, etc.)
- Citation URL: Which page on your site was cited, if any
- Timestamp: When the prompt was run
- AI model: Which engine returned the result (ChatGPT, Perplexity, Claude, etc.)
That's table stakes. Every platform returns some version of this. The differentiation starts with the metadata.
Competitive intelligence
- Competitor mentions: Which other brands appeared in the same response
- Share of voice: Your brand's mention frequency vs competitors across a prompt set
- Position tracking: Did you rank first, or did your competitor?
- Sentiment comparison: Are competitors framed more positively?
Source and citation metadata
- Source type: Was the citation from your blog, docs, a Reddit thread, YouTube, or a third-party review site?
- Source authority: Domain rating or trust score of the cited source
- Citation context: The sentence or paragraph where your brand was mentioned
- Hallucination detection: Did the AI make up facts about your brand?
Attribution and traffic data
- Click-through data: Did users click your citation link? (Requires integration)
- Traffic attribution: Which prompts drove actual visitors to your site
- Conversion tracking: Did AI-referred traffic convert?
- Page-level performance: Which pages get cited most often
Optimization signals
- Prompt difficulty: How competitive is this prompt?
- Prompt volume: How often is this query asked?
- Content gap analysis: Which prompts do competitors rank for but you don't?
- Sentiment score: Is your brand framed positively, neutrally, or negatively?
The platforms that return all of this -- or most of it -- are the ones you can actually build optimization workflows around. The ones that return mention counts and nothing else are dashboards, not tools.
Platform-by-platform schema breakdown
Promptwatch: The most complete data schema
Promptwatch returns one of the richest data schemas in the category. When you pull a response via API or export, you get:
Core fields: Prompt text, full response, brand mention (boolean + position), citation URL, timestamp, AI model (10 engines tracked)
Competitive data: Competitor mentions (up to 5 per response), share of voice across prompt sets, position heatmaps
Source metadata: Source type (blog, docs, Reddit, YouTube, review site), domain authority, citation context (full sentence), hallucination flags
Attribution: Page-level citation tracking, traffic attribution via code snippet or GSC integration, conversion tracking (with UTM tagging)
Optimization signals: Prompt volume estimates, difficulty scores, content gap analysis (which prompts competitors rank for but you don't), sentiment analysis (positive/neutral/negative)
Unique fields: AI crawler logs (which bots hit your site, when, and which pages they read), query fan-outs (how one prompt branches into sub-queries), Reddit/YouTube discussion tracking
Promptwatch also exposes webhook support for real-time alerts and a Looker Studio connector for custom dashboards. If you're building a data pipeline or feeding AI visibility metrics into your BI stack, this is the schema to beat.
Profound: Enterprise-grade schema with deep competitor analysis
Profound tracks 9+ AI engines and returns a comprehensive schema built for enterprise teams:
Core fields: Prompt, response, mention status, citation URL, timestamp, model
Competitive data: Competitor positioning (who ranked where), share of voice trends over time, head-to-head comparisons
Source metadata: Citation source type, domain metrics, context snippets
Attribution: Limited -- no built-in traffic attribution, but you can export data and join it with GA4 or your CRM
Optimization signals: Prompt difficulty, volume estimates, sentiment scoring
Profound's strength is its competitor heatmaps and historical tracking. You can see how your visibility changed week-over-week and compare your performance to 5+ competitors in a single view. The API returns all of this in structured JSON.
Profound
Peec AI: Budget-friendly with solid core data
Peec AI is one of the most affordable platforms (€89/month), and its data schema reflects that -- you get the essentials without the bells and whistles:
Core fields: Prompt, response snippet (not always full text), mention status, citation URL, timestamp, model (ChatGPT, Perplexity, Claude)
Competitive data: Basic competitor mentions (yes/no), no positioning or share of voice
Source metadata: Citation URL only -- no source type, no authority score, no context
Attribution: None
Optimization signals: None
Peec AI is fine if you just need to know "did we get mentioned?" but it won't help you understand why or how to improve. The API returns CSV exports with 8-10 columns. No webhooks, no real-time data.
Otterly.AI: Monitoring-only with limited schema depth
Otterly.AI tracks 6 platforms (ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, Google AI Mode) and returns a straightforward schema:
Core fields: Prompt, response, mention status, citation URL, timestamp, model
Competitive data: Competitor mentions (yes/no), no positioning
Source metadata: Citation URL only
Attribution: None
Optimization signals: None
Otterly.AI is a monitoring dashboard. It tells you what happened, but it doesn't give you the data to figure out why or what to do next. The API returns JSON, but the field count is low (10-12 per response). No webhooks.
Otterly.AI
ZipTie: Deep analysis with unique citation intelligence
ZipTie is built for teams that want to understand the "why" behind AI visibility. Its schema includes:
Core fields: Prompt, full response, mention status, citation URL, timestamp, model
Competitive data: Competitor mentions, positioning, share of voice
Source metadata: Source type (blog, docs, Reddit, YouTube), domain authority, citation context (full paragraph), sentiment analysis
Attribution: Page-level tracking, no traffic attribution
Optimization signals: Prompt difficulty, content gap analysis, sentiment trends
Unique fields: Citation chain analysis (which sources influenced which AI responses), Reddit/YouTube discussion tracking, hallucination detection
ZipTie's API returns 30+ fields per response and supports webhooks. It's one of the few platforms that exposes citation chain data -- you can see which Reddit threads or YouTube videos influenced an AI's answer, even if your brand wasn't directly cited.
AthenaHQ: Monitoring-focused with limited optimization data
AthenaHQ tracks AI visibility across multiple engines but focuses on monitoring over optimization:
Core fields: Prompt, response, mention status, citation URL, timestamp, model
Competitive data: Competitor mentions (yes/no)
Source metadata: Citation URL only
Attribution: None
Optimization signals: None
AthenaHQ's API returns basic JSON exports. No webhooks, no real-time data, no sentiment analysis. It's a dashboard for tracking mentions, not a platform for improving them.
Semrush AI Visibility Toolkit: Fixed prompts, limited schema
Semrush added AI visibility tracking in 2025, but it's tacked onto their traditional SEO platform:
Core fields: Prompt (from a fixed list -- you can't add custom prompts), response snippet, mention status, timestamp, model (limited to a few engines)
Competitive data: None
Source metadata: None
Attribution: None
Optimization signals: None
Semrush's AI visibility module is a checkbox feature, not a serious GEO tool. The data schema is thin, and you can't export custom prompts or track competitors. If you're already paying for Semrush, it's worth turning on, but don't expect API-level access or rich metadata.
Ahrefs Brand Radar: Fixed prompts, no traffic attribution
Ahrefs launched Brand Radar in 2025 to track AI mentions, but like Semrush, it's a feature add-on, not a standalone platform:
Core fields: Prompt (fixed list), response snippet, mention status, timestamp, model (limited)
Competitive data: None
Source metadata: None
Attribution: None
Optimization signals: None
Ahrefs Brand Radar is fine for basic monitoring, but it doesn't return the data you need to optimize. No API access, no custom prompts, no competitor tracking.
Comparison table: What each platform returns
| Platform | Core fields | Competitor data | Source metadata | Attribution | Optimization signals | API/Export | Webhooks |
|---|---|---|---|---|---|---|---|
| Promptwatch | Full | Yes (5+ competitors, positioning, SOV) | Yes (source type, authority, context, hallucination flags) | Yes (traffic, conversions, page-level) | Yes (volume, difficulty, gaps, sentiment) | JSON, CSV, Looker Studio | Yes |
| Profound | Full | Yes (positioning, SOV, trends) | Yes (source type, authority, context) | Limited (export only) | Yes (volume, difficulty, sentiment) | JSON, CSV | No |
| ZipTie | Full | Yes (positioning, SOV) | Yes (source type, authority, context, citation chains) | Page-level only | Yes (difficulty, gaps, sentiment) | JSON, CSV | Yes |
| Peec AI | Basic | Limited (yes/no mentions) | URL only | None | None | CSV | No |
| Otterly.AI | Basic | Limited (yes/no mentions) | URL only | None | None | JSON, CSV | No |
| AthenaHQ | Basic | Limited (yes/no mentions) | URL only | None | None | JSON | No |
| Semrush | Snippet | None | None | None | None | Limited | No |
| Ahrefs | Snippet | None | None | None | None | None | No |
Which fields actually matter for optimization
Not all fields are created equal. Here's what you should prioritize based on your use case:
If you're proving ROI to leadership
You need: Traffic attribution, conversion tracking, page-level performance, competitor positioning
Platforms: Promptwatch (only platform with full attribution), Profound (export and join with GA4)
If you're building content to rank in AI search
You need: Content gap analysis, prompt volume, difficulty scores, citation context, source type
Platforms: Promptwatch (built-in content generation), ZipTie (citation chain analysis), Profound (competitor gaps)
If you're monitoring brand reputation in AI
You need: Sentiment analysis, hallucination detection, citation context, competitor framing
Platforms: Promptwatch (sentiment + hallucination flags), ZipTie (citation chains), Profound (sentiment trends)
If you're tracking competitors
You need: Competitor positioning, share of voice, head-to-head comparisons
Platforms: Promptwatch (5+ competitors, heatmaps), Profound (enterprise-grade competitor analysis), ZipTie (positioning data)
If you're feeding data into your BI stack
You need: API access, webhook support, structured JSON exports, granular filtering
Platforms: Promptwatch (Looker Studio connector, webhooks), ZipTie (webhooks, 30+ fields), Profound (JSON exports)
The platforms that don't return enough data
Some tools market themselves as AI visibility platforms but return so little data that they're barely usable:
- Semrush AI Visibility Toolkit: Fixed prompts, no custom tracking, no API access
- Ahrefs Brand Radar: Fixed prompts, no competitor tracking, no exports
- Peec AI: Basic mention counts, no optimization signals
- Otterly.AI: Monitoring-only, no attribution or sentiment data
- AthenaHQ: Dashboard with limited export options
These tools are fine if you just want to see "did we get mentioned?" but they won't help you understand why or how to improve. If you're serious about GEO, you need a platform that returns actionable data.
How to evaluate an AI visibility platform's data schema
Before you sign up for a platform, ask these questions:
- Can I export the raw data? If the answer is "no" or "only via PDF reports," walk away.
- Does the API return full response text or just snippets? Snippets are useless for analysis.
- Can I track custom prompts? Fixed prompt lists mean you're stuck with what the vendor thinks matters.
- Does it track competitors? If not, you're flying blind.
- Can I see which pages get cited? Page-level data is critical for optimization.
- Does it support webhooks or real-time alerts? If you're building automation, you need this.
- Can I filter by sentiment, source type, or model? Granular filtering = better insights.
- Does it integrate with my analytics stack? If you can't connect it to GA4, your CRM, or your BI tool, it's a silo.
The bottom line
Most AI visibility platforms return the same basic data: prompt, response, mention status, citation URL. The difference is in the metadata. Platforms like Promptwatch, Profound, and ZipTie expose 30+ fields per response, including sentiment, competitor positioning, source metadata, and traffic attribution. Budget tools like Peec AI and Otterly.AI cap out at 10-15 fields and offer no optimization signals.
If you're just tracking mentions, any platform will do. If you're building content, proving ROI, or feeding data into your analytics stack, you need a platform with a rich data schema. Promptwatch is the only platform that combines full attribution, content gap analysis, and AI crawler logs in one place. Profound and ZipTie are strong alternatives for teams that prioritize competitor analysis and citation intelligence.
The rest are dashboards, not tools.