Peec AI
Unverified verified 22 may 2026AI Search Analytics for Marketing Teams
TL;DR
Peec AI is a specialized analytics platform that tracks brand visibility, sentiment, and ranking within AI search engines like ChatGPT, Gemini, and Perplexity. It is designed for marketing teams to execute Generative Engine Optimization (GEO) by identifying the exact web sources that influence AI-generated answers. Its key differentiator is its focus on granular, URL-level citation tracking rather than just high-level mention counts.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Peec AI has rapidly positioned itself as a market leader in the emerging GEO (Generative Engine Optimization) category since its launch in 2025. While traditional SEO tools like Semrush and Ahrefs focus on blue-link rankings, Peec AI addresses the 'black box' of LLM outputs by providing transparency into which sources (e.g., Reddit, G2, niche blogs) are actually being cited by AI agents. This makes it an essential tool for brands seeing a traffic shift from Google to conversational AI platforms. The tool's primary strength lies in its simplicity and research-first design. Instead of overwhelming users with legacy SEO metrics, it offers a clean dashboard focused on 'Share of Model' and 'Prompt-level' analytics. This helps agencies and in-house teams quickly pivot their content strategies based on what AI models are currently surfacing to users. However, it is fundamentally a monitoring tool; it lacks automated content creation or technical audit features found in more expensive 'all-in-one' enterprise platforms. Peec AI is best suited for mid-to-large SaaS brands and digital marketing agencies that need to prove ROI for AI-era search efforts. Its 'unlimited seats' model and white-label Looker Studio integration make it particularly attractive for agencies managing multiple client portfolios. As the field of AI visibility tracking matures, Peec's success will depend on its ability to maintain data accuracy amidst the inherent volatility of LLM citation behaviors.
Pros
- + URL-level citation tracking that identifies the exact sources shaping AI answers
- + Broad model coverage including ChatGPT (GPT-4/o), Perplexity, Gemini, and Claude
- + Agency-friendly features such as unlimited seats and multi-brand workspaces
- + Actionable prompt suggestions based on real-world search volumes and competitive gaps
- + Seamless integration with Looker Studio and CSV exports for customized reporting
Cons
- - Pricing starting at €89/month may be prohibitive for small freelancers or hobbyists
- - Focuses purely on monitoring and research, requiring separate tools for content execution
- - Limited historical data compared to legacy SEO platforms, as the tool is relatively new
- - Strict limits on the number of prompts in lower-tier plans can restrict exploratory research
Sentiment Analysis
Sentiment has remained stable since last capture. The overall sentiment for Peec AI has risen slightly from 0.82 to 0.86 as the tool has matured. It is widely praised by early adopters and agencies for its clean UI and unique ability to track citation sources. While there is minor skepticism regarding the long-term stability of GEO metrics, the general consensus is that Peec is the most practical tool for navigating the transition from traditional search to AI search.
Sentiment Over Time
By Source
10 mentions
Sample quotes (2)
- "I feel the platform is not crowded, unlike tools like Semrush, and Peec AI stands out with a very focused design."
- "Pricing is very fair and reasonable for the depth of analysis provided."
15 mentions
Sample quotes (2)
- "Peec showed the actual Reddit threads shaping answers... It's built for research, not just tracking."
- "GEO is still a bit of a moving target, but Peec is the best tool I've used for mapping how LLMs treat a brand."
25 mentions
Sample quotes (2)
- "Peec AI is the gold standard for GEO tracking in 2026. The source attribution is a game changer."
- "Crossing $4M ARR in its first year says everything about the demand for AI search analytics."
1 mention
Sample quotes (1)
- "Average experience so far, the tool is simple but still early days."
Agent Readiness
64/100Peec AI is highly ready for autonomous agent integration. Its public REST API allows agents to programmatically fetch visibility data, sentiment scores, and citation URLs. The inclusion of a Zapier integration and a Looker Studio community connector further simplifies the process of piping AI search data into automated marketing workflows. While it lacks a dedicated sandbox, the well-documented API and existence of a public changelog indicate a professional developer experience.
Last checked May 1, 2026
MCP Integrations
1 server11 toolsConnect your AI assistant to your Peec AI account to monitor and analyze your brand's visibility across AI search engines like ChatGPT, Perplexity, and Gemini. Ask questions about brand visibility, competitor comparisons, source citations, and trends: all in plain language, directly from your AI tools.
11 tools
list_projectsList active projects the authenticated user has access to. By default, only projects with an active status (CUSTOMER, PITCH, TRIAL, ONBOARDING, API_PARTNER) are returned. Set include_inactive to true to include ended/paused projects. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name, status. The id is used as project_id in other tools. Call this first to discover available projects.list_topicsList topics in a project. Topics are folder-like groupings — each prompt belongs to exactly one topic. Use this tool to resolve topic names to IDs before filtering (topic_id filter/dimension, list_prompts), and to label topic IDs from report output with their human-readable names before presenting results. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name.list_tagsList tags in a project. Tags are cross-cutting labels that can be assigned to any prompt. Use this tool to resolve tag names to IDs before filtering (tag_id filter/dimension, list_prompts), and to label tag IDs from report output with their human-readable names before presenting results. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name.list_brandsList brands tracked in a project — includes the user's own brand and competitors. Use this tool to resolve brand names to IDs before filtering reports (brand_id filter), and to label brand IDs from report output with their human-readable names before presenting results. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name, domains, is_own. is_own indicates which brand belongs to the user.list_modelsList AI engines (models) tracked by Peec. Use this tool to resolve model names (e.g., "ChatGPT", "Perplexity", "Gemini") to IDs before filtering reports (model_id filter/dimension), and to label model IDs from report output with their human-readable names before presenting results. Match user-supplied names against the name column; the id column is the canonical string to pass back as model_id. is_active indicates whether the model is enabled for this project — inactive models will return empty data in reports. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name, is_active.list_promptsList prompts (conversational questions tracked daily across AI engines) in a project. Supports filtering by topic_id and tag_id. Use this tool to resolve prompt text to IDs before filtering reports (prompt_id filter/dimension), and to label prompt IDs from report output with their actual text before presenting results. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, text, tag_ids (array of tag ID strings), topic_id (string or null).list_chatsList chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, prompt_id, model_id, date.get_chatGet the full content of a single chat (one AI engine's response to one prompt on one date). Returns: - messages: the user prompt and assistant response(s) - brands_mentioned: brands detected in the response with their position - sources: URLs the model retrieved, with citation counts and position - queries: search queries the model issued - products: product gallery entries extracted from the response - prompt: { id } - model: { id } Use list_chats to discover chat IDs for a project.get_brand_reportGet a report on brand visibility, sentiment, and position across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount, total}. Each row is an array of values matching column order. Columns: - brand_id — the brand ID - brand_name — the brand name - visibility: 0–1 ratio — fraction of AI responses that mention this brand. 0.45 means 45% of conversations. - mention_count: number of times the brand was mentioned - share_of_voice: 0–1 ratio — brand's fraction of total mentions across all tracked brands - sentiment: 0–100 scale — how positively AI platforms describe the brand (most brands score 65–85) - position: average ranking when the brand appears (lower is better, 1 = mentioned first) - Raw aggregation fields (for custom calculations): visibility_count, visibility_total, sentiment_sum, sentiment_count, position_sum, position_count When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, brand_id, country_code, chat_id.get_domain_reportGet a report on source domain visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - domain: the source domain (e.g. "example.com") - classification: domain type — CORPORATE (official company sites), EDITORIAL (news, blogs, magazines), INSTITUTIONAL (government, education, nonprofit), UGC (social media, forums, communities), REFERENCE (encyclopedias, documentation), COMPETITOR (direct competitors), OWN (the user's own domains), OTHER, or null - retrieved_percentage: 0–1 ratio — fraction of chats that included at least one URL from this domain. 0.30 means 30% of chats. - retrieval_rate: average number of URLs from this domain pulled per chat. Can exceed 1.0 — values above 1.0 mean multiple pages from the same domain are retrieved per conversation. - citation_rate: average number of inline citations when this domain is retrieved. Can exceed 1.0 — higher values indicate stronger content authority. When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, domain, url, country_code, chat_id.get_url_reportGet a report on source URL visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - url: the full source URL (e.g. "https://example.com/page") - classification: page type — HOMEPAGE, CATEGORY_PAGE, PRODUCT_PAGE, LISTICLE (list-structured articles), COMPARISON (product/service comparisons), PROFILE (directory entries like G2 or Yelp), ALTERNATIVE (alternatives-to articles), DISCUSSION (forums, comment threads), HOW_TO_GUIDE, ARTICLE (general editorial content), OTHER, or null - title: page title or null - citation_count: total number of explicit citations across all chats - retrievals: total number of times this URL was used as a source, regardless of whether it was cited - citation_rate: average number of inline citations per chat when this URL is retrieved. Can exceed 1.0 — higher values indicate more authoritative content. When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, domain, url, country_code, chat_id.
Last checked May 22, 2026
[ features ]
AI Engine Coverage
Coverage and support for various AI models, LLMs, and search engines.
List of AI models and LLMs supported for tracking (e.g., ChatGPT, Gemini).
How often metrics are updated (e.g., real-time, daily).
Support for tracking in multiple countries or regions.
Monitoring Metrics
Key performance indicators and analytics provided for brand presence.
Tracks brand mention frequency or share in AI responses.
Monitors brand's ranking or position in AI-generated results.
Analyzes tone and perception of brand in AI outputs.
Compares brand performance against competitors.
Identifies sources cited in AI responses for the brand.
Optimization Tools
Features for improving brand presence through content and strategy adjustments.
Provides tailored suggestions for content to boost AI visibility.
Pre-built templates for AI-optimized content formats.
Allows users to define and track custom customer-like queries.
Human oversight in AI-generated content workflows.
Integrations and Pricing
Ecosystem compatibility, extensibility, and cost structure.
Pre-built connections to popular tools.
Offers a free trial period for testing.
Publicly listed pricing without requiring contact.
Single Sign-On integration for teams.
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