Krastie AI v3 product launch
Version 3 launch announcement for the Krastie AI product
Signal
Visibility
Sign in free to unlock the full scoring breakdown, root-cause analysis, and solution blueprint.
Sign up freeAlready have an account? Sign in
Deep Analysis
Root causes, cross-domain patterns, and opportunity mapping
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Solution Blueprint
Tech stack, MVP scope, go-to-market strategy, and competitive landscape
Sign up free to read the full analysis — no credit card required.
Already have an account? Sign in
Similar Problems
surfaced semanticallyAI Search Visibility Checker Tool
This post is a product listing, not a user problem description.
Brands Cannot Measure or Improve LLM Recommendation Visibility
As AI search tools increasingly mediate discovery, brands have no reliable way to measure whether LLMs recommend them or understand why they are excluded. The lack of visibility into AI-driven brand mentions creates a blind spot in modern marketing analytics.
Brands have no visibility into how AI search engines represent or omit them
As users increasingly discover products through ChatGPT, Perplexity, Claude, and Gemini rather than traditional search, brands cannot measure their presence or identify gaps in AI-generated results. Existing SEO analytics tools do not cover LLM-based search engines. Marketers are flying blind on an increasingly important discovery channel.
Marketers cannot track or grow brand presence in LLM-generated search results
Brand visibility is increasingly determined by how AI systems like ChatGPT and Perplexity represent companies in generated responses. There are no established tools for identifying which prompts trigger brand mentions, diagnosing content gaps, or attributing traffic growth to LLM-specific optimization efforts. Marketing teams lack any measurement framework for this channel.
SEO Teams Lack Repeatable Tools to Track Brand Visibility in AI Search Answers
SEO and GEO teams currently track brand mentions, rankings, and citations across AI answer engines using manual spreadsheets and screenshots. This ad hoc process doesn't scale and lacks prompt-level visibility into content and citation gaps.
Problem descriptions, scores, analysis, and solution blueprints may be updated as new community data becomes available.