Peec AI
Unclaimed verified 3 jul 2026AI Search Analytics for Marketing Teams
TL;DR
Peec AI is a specialized analytics platform designed for Generative Engine Optimization (GEO), helping marketing teams track brand visibility and sentiment within AI models like ChatGPT and Perplexity. It is primarily built for SaaS and e-commerce teams that need to understand how AI perceives their brand and which external sources (like Reddit or niche blogs) are influencing those AI responses. Its key differentiator is its focus on source-level attribution and 'Share of Model' tracking at a mid-market price point.
What Users Actually Pay
No user-reported pricing yet.
Our Take
Peec AI occupies a strategic sweet spot in the emerging GEO market: it is more affordable than enterprise heavyweights like Profound or Gauge, yet significantly more research-oriented than entry-level trackers. Its interface is exceptionally clean, and its focus on 'source citations'—showing the exact URLs and community threads that LLMs use to form answers—provides actionable intelligence that traditional SEO tools lack. It effectively turns the 'black box' of AI search into a map of influential digital PR targets. However, the platform is strictly a diagnostic tool. While it excels at identifying where a brand is losing visibility, it does not offer built-in content generation or automated on-page optimization. Teams using Peec AI must have the internal capacity to act on its data by creating content or building authority on the cited sources it identifies. Additionally, while the entry price is accessible, the cost can scale quickly as users add monitoring for extra models like Claude or Gemini. Peec AI is best suited for growth-stage SaaS companies and agencies that already have a solid content foundation and now need to defend or expand their 'Share of Chat.' It is less ideal for very small businesses looking for an all-in-one 'fix my SEO' solution, as the lack of execution tools requires a separate workflow to move the needle on the metrics it tracks.
Pros
- + Deep Source Attribution: Identifies the specific Reddit threads, YouTube videos, and blog posts cited by AI models to inform their answers.
- + Agency-Friendly Pricing: Offers unlimited team seats on all plans, making it easy for agencies to collaborate with clients without extra user fees.
- + Entity-Level Tracking: Allows for granular monitoring of specific SKUs, product features, or categories rather than just high-level brand mentions.
- + AI-Driven Research: Provides suggested prompts based on actual search volumes and competitor gaps, helping teams prioritize what to track.
Cons
- - Add-on Pricing Complexity: While the base plan is affordable, adding platforms like Claude, Gemini, or DeepSeek incurs additional monthly fees per engine.
- - No Execution Workflow: The tool tells you 'what' is wrong but lacks built-in content creation or optimization features to help 'fix' the visibility gaps.
- - Lack of Enterprise Security: As a newer startup (founded 2025), it currently lacks SOC 2 Type II certification compared to enterprise-tier competitors.
Sentiment Analysis
Sentiment has remained stable since last capture. The overall sentiment remains strongly positive (0.82), though it has dipped slightly from the previous 0.86 as more users provide critical feedback regarding the 'measurement vs. execution' gap. Users praise the tool's UI, research depth, and specific source tracking, but some SMB users question the ROI if they don't have the team to act on the data.
Sentiment Over Time
By Source
12 mentions
Sample quotes (2)
- "Peec showed the actual Reddit threads shaping answers... It gave me a clearer sense of where people were talking and where we could add something meaningful instead of guessing."
- "They mainly measure visibility in AI responses, not improve it. That's useful for agencies or big brands that need reporting dashboards."
5 mentions
Sample quotes (1)
- "Peec avoids the issues we see with other platforms where there's an overload of features. It keeps things simple - set up prompts, see visibility, and act on citations."
1 mention
Sample quotes (1)
- "SMB-MID WINNER: Strength in entity + product-level tracking (how specific SKUs show up in generative answers). Direct Slack support from team."
Agent Readiness
54/100Peec AI is exceptionally 'Agent Ready,' specifically due to its native MCP (Model Context Protocol) server, which allows AI agents like Claude or Cursor to query its analytics directly. While it lacks pre-built nodes for Zapier or Make, it offers a robust REST API and a dedicated Customer API for custom reporting. The documentation is high-quality (hosted on Mintlify) and includes clear guidance on API key scoping and tool-based interactions, making it a top choice for developers building autonomous marketing workflows.
Last checked Jun 20, 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 Jun 21, 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.
Compare With
Reviews
No reviews yet. Be the first to review Peec AI!