Build a System to Track Your AI Search Mentions

Most businesses track brand mentions by manually typing their name into ChatGPT or Gemini once a month. This is inconsistent and misses mentions that appear for only a short time. Standard SEO tools like Ahrefs or SEMrush are built for web crawlers and backlinks; they have no capability to monitor the conversational outputs of large language models. Their architecture is designed to parse HTML, not the unstructured text from a generative AI.

Consider a marketing manager at a 20-person software company. She spends two hours at the end of each month testing 5-10 queries in ChatGPT and Claude to see if her company is mentioned. She copies the results into a spreadsheet. This process is slow, impossible to scale across the 9+ major AI engines, and provides no historical trend data. She has no way to know if a competitor was mentioned for the same query last week.

The structural problem is that AI search engines do not have a 'backlink' equivalent. A mention is ephemeral, generated in real-time based on the model's training data and the specific prompt. There is no public index to crawl. The only way to track mentions is to simulate user queries at scale by calling each model's API directly, which existing SEO platforms are not built to do. They lack the infrastructure for high-volume API calls to third-party LLMs and the logic to parse the probabilistic, non-deterministic responses.

Syntora built its own 9-engine Share of Voice monitor because no off-the-shelf tool could do the job. For a client, the process would start with defining the key discovery queries your customers use. We would identify 20-30 high-intent questions, product comparison prompts, and problem-based searches relevant to your business to form the core query set.

The monitoring system is a Python application running on AWS Lambda, triggered on a weekly schedule. It uses httpx to make parallel, asynchronous API calls to engines like ChatGPT, Claude, Gemini, and Perplexity. Each response is parsed for your brand name and competitor names. Results, including the full text and a citation flag, are stored in a Supabase Postgres database. This architecture costs less than $50 per month to run for up to 100 queries per week.

The final deliverable is the deployed monitoring system and a simple data dashboard. You get weekly email summaries and a link to a dashboard showing your share of voice over time versus competitors. You receive the full Python source code and a runbook, giving you complete ownership of the system. There is no ongoing subscription; it runs in your own cloud account.