Track Your Company's Mentions in ChatGPT and Claude

Manufacturing marketing teams rely on tools like SEMrush, Ahrefs, and Google Alerts for performance metrics. These platforms are excellent for monitoring the public, indexed web. They track keyword rankings, backlinks, and brand mentions on websites, but they have a structural blind spot: they cannot see inside the conversational interfaces of ChatGPT, Claude, Gemini, or Perplexity.

Here is a concrete scenario. A plant manager for a specialty chemicals company has a problem with supply chain compliance. She does not search Google for "compliance software." She asks Claude, "How can I automate safety data sheet compliance reporting for our chemical transport logistics?" The AI generates a unique, conversational answer recommending three software vendors and a consultant. If your company is mentioned, your existing SEO tools will miss it completely. The interaction is ephemeral and never hits the public web.

The core problem is an architectural mismatch. SEO tools were built to crawl a static web of hyperlinked documents. AI search is a dynamic, generative interface. There is no stable URL to rank, no backlink to count, and no public log to parse. Each answer is generated on-demand for a single user in a private session. Without a system to directly query these models, you have no visibility into whether you are being recommended to your highest-intent buyers.

Syntora built an internal AI Share of Voice monitor because we saw prospects arriving from AI-driven discovery. The first step in building a similar system for a manufacturing client would be to define the 'buyer problem' queries. We would work with your sales and engineering teams to identify the top 50 questions your prospects ask, from high-level research like "best ERP for mid-size discrete manufacturing" to specific technical problems such as "how to reduce CNC machine downtime using sensor data."

The technical core of the system is a Python script that uses official APIs for models like Claude and Gemini, and browser automation for models without them. The script runs on a weekly schedule, cycling through the defined prompts and saving every full response to a Supabase database. Using FastAPI, a simple internal dashboard is built to view the raw responses and a summary table that flags any mention of your company, your top 3 competitors, or key industry terms.

The delivered system is a private dashboard providing weekly reports. You see exactly which AI engines recommend you, for which queries, and in what context. The system provides the raw text, allowing your content team to see which of your web pages are being cited and to refine your AEO strategy. We deployed our internal monitor on AWS Lambda for serverless, low-cost execution, which runs for under $30 per month.