LLM Share-of-Model & Visibility Tracker

Promising. This opportunity addresses the emerging shift from traditional SEO to Generative Engine Optimization (GEO). Marketing departments are currently struggling to quantify their visibility within LLMs, and evidence from the n8n community shows users are already attempting to build custom automation scripts for this exact purpose.

As search behavior shifts toward AI-native tools (ChatGPT, Perplexity, Gemini), brands no longer know how they are perceived or recommended. Unlike Google, there are no 'rank trackers' for conversational AI. Marketers are manually prompting these models to see if they are mentioned, which is unscalable, prone to hallucination variability, and impossible to report to stakeholders consistently.

• SEO Agencies: Need to offer 'AI SEO' services to clients to stay relevant.
• SaaS Marketing Teams: Highly sensitive to how they are categorized by AI models for competitive comparisons.
• E-commerce Brands: Want to ensure they are the 'recommended' product for specific category queries.

Two distinct mentions in the n8n community (a hub for power-users and technical marketers) explicitly describe the need for an 'AI visibility tracker' or 'AI SEO' tool. Users are looking for freelancers to build custom automations for this, indicating a lack of accessible off-the-shelf SaaS solutions for the mid-market.

A focused tracking dashboard:

1. Keyword Input: User provides a list of keywords/prompts (e.g., 'best CRM for small business').
2. Multi-Model Querying: The app uses APIs for GPT-4, Claude 3, and Gemini to run these prompts.
3. Extraction: It uses LLMs to parse the response and identify if the user's brand was mentioned and in what context.
4. Reporting: A weekly PDF or dashboard showing 'Visibility %' and 'Competitor Share'.

Marketing tools are high-value because they provide 'Peace of Mind' and 'Proof of Work'. An agency can charge a client $2,000/mo for SEO services; paying $99/mo for a tool that generates the reports proving their work is working is an easy 'yes'.

Medium. The main challenge is the API cost of LLMs and potential rate limits. However, using smaller models (GPT-4o-mini or Gemini Flash) for the extraction phase keeps costs low. No complex custom engineering is required; it's primarily a CRUD app with scheduled API jobs.

Enterprise players like Profound are emerging, but there is significant room for a 'lean' version targeting smaller agencies who cannot afford $1k+/month enterprise contracts. Currently, the most common alternative is 'doing nothing' or manual, inconsistent checking.

The best path is Direct Outbound to SEO Agency owners on LinkedIn. Position the tool as a way for them to upsell their existing clients on 'AI Readiness Audits'. Additionally, participating in technical communities like n8n and Make.com where people are already trying to build this can capture early adopters.

Reaching 100 subscribers at $49/month ($4,900 MRR) is highly realistic given the sheer number of digital marketing agencies worldwide (over 50,000 in the US alone). A solo developer can manage this scale easily.