GrackerAI

GrackerAI is the author's own company, so this entry carries the conflict disclosure above and readers should apply additional skepticism here compared to vendors where the author has no commercial interest. What follows is a factual description of the platform against the same rubric used for every other vendor.

What it is. GrackerAI positions itself as an enterprise AEO and GEO platform rather than a single-metric tracker. The product is organized into three pillars:

• Know (AI Visibility Intelligence): AI monitoring, an AI Visibility Score (one normalized number benchmarked to your category), AI search analytics, Brand Perception (the qualities AI associates with you and your rivals), Brand Sentiment (how warmly and how accurately AI talks about you), LLM Citations (the source map behind AI answers, and where to earn the next citation), competitor LLM monitoring, an AI Prompt Generator, and a Technical AEO Audit.
• Act (AI Visibility Optimization): a Recommendation Engine and a Content Engine that produces LLM-optimized articles (structured, sourced, and formatted for citation, including comparison and compliance pieces), publishing into 15 or more CMSs (WordPress, HubSpot, Webflow, Contentful, Sanity, Sitecore, Ghost, and others) or a custom API.
• Connect (AI Visibility Integration): a GrackerAI MCP (Model Context Protocol) server plus integrations with Google Search Console, Bing, Slack, and Zapier.

The scorecard. The measurement layer covers the metrics buyers ask for by name: Brand Visibility (the AI Visibility Score), Content Depth and Coverage (a content topic-depth audit), Brand Sentiment analysis, Citation Rate with citation-source attribution, competitor share of voice, and AI crawler optimization through a Technical AEO Audit that checks how easily AI crawlers can read, parse, and cite a site and returns the specific fixes.

Monitoring cadence. Frequency scales by tier: weekly prompt monitoring on Starter, daily on Scale, and hourly on Enterprise. That hourly cadence, combined with page-level monitoring across up to 100K pages with prompt-to-page mapping, is the platform's main scaling claim for large sites.

How prompts are sourced. Rather than only synthesizing queries, GrackerAI runs on what it calls a demand-grounded library of real buyer prompts, sourced through partnerships with data providers and combined with an internal prompt-generation engine. At the enterprise tier it adds a prebuilt cybersecurity prompt library mapped to buyer journeys across SIEM, IAM, CSPM, EDR, endpoint, and compliance queries.

Multi-product and enterprise. For large security vendors, GrackerAI tracks each product line (endpoint, identity, cloud, network) under one brand and uses cybersecurity-tuned analysis models that understand compliance terminology, product categories, and threat-model framing. The enterprise tier adds single-tenant deployment on isolated infrastructure, SSO/SAML and API access, white-label reporting, a dedicated account manager, a 4-hour support response SLA, and quarterly business reviews.

Pricing is public at the low end and contact-sales at the top. Starter is $79/month billed annually ($99 monthly) with 100 weekly prompts across 3 engines; Scale is $399/month annually ($499 monthly) with 400 daily prompts across 5 engines plus the AI crawlability and content-depth audits; Enterprise is custom, with 1,000 or more hourly prompts across all 9 supported engines, unlimited articles with CMS publishing, and the security and deployment features above. A 7-day free trial with no credit card is offered on the self-serve tiers.

Honest limitations. The analysis models and prebuilt prompt library are tuned for cybersecurity, which is a strength for security brands and a weaker out-of-the-box fit for other verticals. The customer base is smaller than category incumbents such as Profound and AthenaHQ, and as a younger product the methodology has had less time to be validated by adversarial third-party analysis. Readers should evaluate GrackerAI against the same rubric as every other vendor here: engine coverage, methodology transparency, data exportability, pricing, and fit for the specific use case. The author's involvement is relevant context, not a reason to weight the entry up or down.