Monitor Your Company's Visibility in ChatGPT and Claude

Marketing teams at construction firms might try asking ChatGPT or Gemini a few questions manually, like “who is the best general contractor for hospital projects in Austin?”. This manual approach fails because AI model responses are not stable. An answer you see today might be completely different tomorrow after a model update, making one-off checks useless for tracking progress.

Consider a commercial GC who asks Claude for local pre-construction specialists and sees their firm listed. The marketing director is thrilled. Two weeks later, the CEO tries the same prompt and gets a list of three competitors. Without a systematic tracking process, they have no data on why the answer changed, how often they appear, or what content influences the AI's recommendations. This creates confusion, not clarity.

Your existing marketing tools cannot solve this. Platforms like SEMrush or Moz are designed to crawl the web and track keyword rankings on Google. They are not built to execute hundreds of conversational prompts against LLM APIs like OpenAI's. Their crawlers look for HTML links and sitemaps, not for parsing natural language responses from a generative AI. They measure a different kind of discovery entirely.

The structural problem is that AI recommendations are probabilistic, not deterministic. Unlike a Google search result, an AI's answer is generated on the fly and can vary based on phrasing and model version. Reliable tracking requires a statistical approach: running a large, consistent set of prompts across multiple AIs over time to establish a baseline and measure change. Standard marketing dashboards lack the architecture for this.

The first step is a discovery workshop to map your ideal customer profiles and the specific problems they need to solve. We define the questions a project executive or developer would ask an AI, such as “recommend a design-build firm with experience in cold storage facilities”. These questions form the core prompt library for the monitoring system, ensuring the data reflects real buyer behavior.

Syntora built our internal monitor using Python and a FastAPI service. For your construction firm, a similar system would use the Claude and OpenAI APIs to run your prompt library against the models on a weekly schedule. A Supabase PostgreSQL database stores every AI-generated response. An AWS Lambda function then parses the text to identify mentions of your firm and your top three competitors, creating structured data from the unstructured text.

The delivered system is a simple dashboard, hosted on Vercel, that shows your Share of Voice over time. You can see which specific prompts generate recommendations for you versus your competitors and track how your visibility changes weekly across all monitored AI engines. You receive the full source code and a runbook explaining how to add new prompts or competitors to the system.