Peec AI

Peec AI

Unclaimed verified 22 aug 2026
score · 43  ]

AI Search Analytics for Marketing Teams

Pricing: Paid - €89/month Company: Peec AI Founded: 2025 Last verified: 2026-08-22
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TL;DR

Peec AI is a specialized analytics platform designed for marketing teams to monitor brand visibility, ranking, and sentiment within Generative AI search results (e.g., ChatGPT, Perplexity). It differentiates itself by focusing exclusively on Generative Engine Optimization (GEO) metrics rather than traditional SEO, offering prompt-based tracking and competitor comparisons tailored to LLM environments.

What Users Actually Pay

No user-reported pricing yet.

Our Take

Peec AI occupies an emerging niche at the intersection of marketing analytics and generative AI. As brands increasingly worry about "invisible" traffic loss to AI summaries, Peec provides a necessary lens into share-of-voice within LLMs. Its market position is that of an early mover; founded in 2025, it addresses a pain point that traditional tools like SEMrush or Ahrefs are only beginning to tackle. The platform's emphasis on simplicity and daily trend tracking suggests it is built for operational marketing teams rather than data scientists, prioritizing actionable GEO strategies over raw data dumps. Strengths lie in its specificity; by allowing users to mimic customer prompts and track responses across multiple models, it offers granular visibility that generalist tools lack. The integration with Looker Studio and CSV exports ensures data portability, which is critical for agencies needing to report to clients. However, as a 2025-founded product, its primary limitation is maturity. The ecosystem around GEO is still defining itself, meaning benchmark data may be limited compared to established SEO metrics. This tool is best suited for mid-to-enterprise marketing teams who have already secured traditional search visibility and are now prioritizing AI search presence. It is less suitable for small businesses on tight budgets, given the €89/month starting price point and the specialized nature of the insights which require a mature content strategy to act upon.

Pros

  • + Specialized GEO Metrics: Tracks visibility, position, and sentiment specifically within AI chat interfaces, filling a gap left by traditional SEO tools.
  • + Actionable Strategy Recommendations: Provides specific suggestions such as creating G2 profiles or pursuing digital PR to influence AI source selection.
  • + Integration Flexibility: Supports exports to CSV, Looker Studio integration, and an API for custom reporting workflows.

Cons

  • - Limited Third-Party Validation: As a 2025 launch, there are no independent user reviews on major platforms (G2, Capterra) to verify long-term reliability or support quality.
  • - Niche Utility: Features are highly specialized for AI search; teams not yet prioritizing GEO may find the ROI difficult to justify immediately.
  • - Pricing Transparency: Paid plans start at €89/month with no publicly advertised free tier, which may hinder small teams from testing the tool before commitment.

Agent Readiness

27/100

Peec AI shows Moderate Agent Readiness. While the existence of an API and Looker Studio integration indicates a willingness to support automated workflows, the lack of publicly accessible API documentation, sandbox environments, or no-code integration partners (like Zapier) creates friction for autonomous agents. Developers would likely need to contact support for API access details, limiting immediate plug-and-play automation capabilities.

API Surface40
Public APIunknown
Protocol Support40
MCP (11 tools)
SDK Availability35
npm: mcp-server-peecai (official)npm: @jplansink/n8n-nodes-peec-ai (official)npm: n8n-nodes-peecai (official)npm: ai
Integration Ecosystem0
Looker StudioCSV ExportCustom API
Developer Experience0
Docs: Limited public documentation

Last checked Aug 22, 2026

MCP Integrations

1 server11 tools
peec-ai-mcp
peec-ai-mcppeec-ai/peec-ai-mcp
smitheryRemote

Connect 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 Aug 20, 2026

[ features ]

AI Engine Coverage

Coverage and support for various AI models, LLMs, and search engines.

Supported AI Models

List of AI models and LLMs supported for tracking (e.g., ChatGPT, Gemini).

ChatGPT  ] Perplexity  ] Claude  ]
Tracking Frequency

How often metrics are updated (e.g., real-time, daily).

Daily
Geographic Coverage

Support for tracking in multiple countries or regions.

Global  ]

Monitoring Metrics

Key performance indicators and analytics provided for brand presence.

Visibility/Share of Voice Tracking

Tracks brand mention frequency or share in AI responses.

yes  ]
Position/Ranking Tracking

Monitors brand's ranking or position in AI-generated results.

yes  ]
Sentiment/Reputation Analysis

Analyzes tone and perception of brand in AI outputs.

yes  ]
Competitor Benchmarking

Compares brand performance against competitors.

yes  ]
Source/Citation Tracking

Identifies sources cited in AI responses for the brand.

yes  ]

Optimization Tools

Features for improving brand presence through content and strategy adjustments.

AI Content Recommendations

Provides tailored suggestions for content to boost AI visibility.

yes  ]
Content Generation Templates

Pre-built templates for AI-optimized content formats.

no
Custom Prompt Support

Allows users to define and track custom customer-like queries.

yes  ]
Human-in-the-Loop Controls

Human oversight in AI-generated content workflows.

no

Integrations and Pricing

Ecosystem compatibility, extensibility, and cost structure.

Third-Party Integrations

Pre-built connections to popular tools.

Looker Studio  ]
Free Trial Available

Offers a free trial period for testing.

yes  ]
Pricing Transparency

Publicly listed pricing without requiring contact.

yes  ]
Enterprise SSO Support

Single Sign-On integration for teams.

yes  ]

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