Automate Your AI Search Share of Voice Tracking

Most teams start by manually prompting ChatGPT or Gemini with their top 10 keywords. This is slow and produces inconsistent results. LLM responses vary between sessions, and manually tracking competitor mentions across hundreds of questions on a weekly basis is an impossible task for a small team. The manual process simply does not scale.

Traditional SEO tools like Ahrefs or Semrush are built for Google's ten blue links, not for AI citations. They cannot parse the unstructured text of an LLM response to identify if your URL was cited, in what position, and which competitors were also mentioned. Their SERP feature analysis completely misses the context of a generative answer, leaving you blind to your actual AI visibility.

A 25-person fintech company tried to track their visibility on Perplexity for 'best business credit cards'. One marketing person spent 4 hours every Monday running 50 queries, pasting results into a spreadsheet, and counting mentions. After a month, the data was too noisy to be useful, they had no historical trend, and they had no idea if their content changes were having any effect.

Syntora would begin an engagement by collaborating with your team to define the initial scope. This includes identifying your core domain, key competitor domains (typically up to five), and a seed list of 200-500 target questions relevant to your industry, often mined from sources like Reddit and Google PAA.

The technical approach would involve deploying a robust, scheduled workflow, likely using GitHub Actions, to query multiple LLM APIs weekly. This querying system would be designed to retrieve responses from a comprehensive list of engines such as Gemini, Perplexity, Brave, Claude, ChatGPT, Grok, DeepSeek, KIMI, and Llama, ensuring broad coverage.

A Python-based system, leveraging libraries like httpx for concurrent requests, would then collect and process the raw text responses. We have extensive experience building similar document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies here. This system would parse responses to meticulously extract all URL citations and brand mentions using advanced regular expressions and string-matching algorithms. The extracted citation data—including the associated question, engine, citation position, and competitor flag—would be securely stored in a Supabase table, forming a historical dataset for ongoing analysis.

The core analytical engine would calculate your share of voice by comparing your total citations against all tracked domains, offering insights on a per-engine and per-question basis. Syntora would engineer a custom dashboard, potentially using a tool like Retool, to visualize these trends over time, providing a clear view of citation growth and competitive shifts week-over-week. The deliverables would include the deployed tracking system, the analytics dashboard, and a documented content strategy feedback loop. This feedback loop would help prioritize content creation for topics where competitors show high visibility and your brand has room to grow, potentially incorporating LLM APIs like Gemini to score citation relevance.