How much does Peec AI cost?

Peec AI uses a paid pricing model. Starting price: €89/month.

What are the main advantages of Peec AI?

Authentic UI scraping captures real-world user search outputs rather than sanitized LLM API responses. Granular sentiment scoring and citation source discovery identify which domains drive AI mentions. Agency-friendly architecture supporting multiple client workspaces, tagging, and Looker Studio exports. Native Model Context Protocol (MCP) server and REST API for agentic workflows and developer integrations.

What are the drawbacks of Peec AI?

AI model gating where standard tiers monitor only three engines by default, requiring paid add-ons for more. Purely diagnostic tool lacking built-in content generation, outreach automation, or remediation workflows. No direct conversion tracking or attribution linking AI citations to downstream inbound pipeline.

What are the best alternatives to Peec AI?

Alternatives to Peec AI in this category include Ahrefs, SE Ranking, OtterlyAI, Semrush, Profound.

Peec AI

Unclaimed verified 4 oct 2026

[ score · 47 ]

AI Search Analytics for Marketing Teams

Pricing: Paid - €89/month Company: Peec AI Founded: 2025 Last verified: 2026-10-04

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Updated Sep 21, 2026

TL;DR

Peec AI is an AI search analytics and Generative Engine Optimization (GEO) platform that monitors brand visibility, rankings, sentiment, and citation sources across leading AI models like ChatGPT and Perplexity. Designed for marketing teams and agencies, it differentiates itself through authentic browser-level UI scraping (capturing real user outputs) and native Model Context Protocol (MCP) server integration.

What Users Actually Pay

No user-reported pricing yet.

Our Take

Peec AI occupies a focused niche in the emerging Generative Engine Optimization (GEO) market. While legacy SEO suites attempt to bolt on basic AI tracking metrics, Peec AI was built specifically to analyze LLM citation mechanics and brand perception. Its core value lies in continuous prompt-level rank tracking, sentiment analysis, and source attribution that pinpoints exactly which digital PR, review sites, or web articles feed AI answers. By leveraging browser automation rather than backend APIs, Peec AI mirrors logged-out user experiences more faithfully than many competitors. Marketing agencies and mid-market growth teams benefit from multi-client workspaces, Looker Studio reporting, and an MCP server that lets autonomous AI agents directly query tracking metrics. However, Peec AI is predominantly an observational monitoring platform rather than an execution suite. It flags visibility gaps and citations, but leaves content remediation and outreach entirely to the user. Additionally, entry-level plans restrict monitoring to three default AI engines, requiring recurring add-ons to cover additional models. Overall, Peec AI is best suited for SEO/GEO specialists, brand managers, and digital agencies that need dependable, daily generative search monitoring and agency-friendly multi-project reporting without enterprise bloat.

Alternatives

Ranked by Revuo score — paid tiers never affect order.

Ahrefs

AI Marketing Platform Powered by Big Data

SE Ranking

AI SEO Software That Gets Results

OtterlyAI

AI Search Monitoring Tool: Track ChatGPT, Perplexity & Google AIO

Semrush

Data-Driven Marketing Tools to Grow Your Business

Profound

Optimize Your Brand's Visibility in AI Search

BrandKarma · Revuo-affiliated

AI Visibility and SEO for e-Commerce

Pros

+ Authentic UI scraping captures real-world user search outputs rather than sanitized LLM API responses
+ Granular sentiment scoring and citation source discovery identify which domains drive AI mentions
+ Agency-friendly architecture supporting multiple client workspaces, tagging, and Looker Studio exports
+ Native Model Context Protocol (MCP) server and REST API for agentic workflows and developer integrations

Cons

- AI model gating where standard tiers monitor only three engines by default, requiring paid add-ons for more
- Purely diagnostic tool lacking built-in content generation, outreach automation, or remediation workflows
- No direct conversion tracking or attribution linking AI citations to downstream inbound pipeline

Agent Readiness

50/100

Peec AI provides strong baseline agent readiness via a dedicated Customer REST API (/customer/v1) and an official remote Model Context Protocol (MCP) server endpoint (api.peec.ai/mcp). Its Mintlify-hosted documentation includes llms.txt discovery and pre-built MCP tools that allow AI agents to list projects, fetch visibility metrics, and analyze brand share of voice programmatically.

API Surface85

Public APIRESTopenApi

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

Google Looker StudioModel Context Protocol (MCP) ServerCSV Exports

Developer Experience65

Docs: goodVersioningChangelog

Last checked Sep 21, 2026

MCP Integrations

1 server11 tools

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.