Track Your Company's Mentions in ChatGPT and Claude
You track AI recommendations by running weekly queries on a multi-engine share of voice monitor. The monitor asks AI models questions your buyers ask and logs when they cite your business.
Key Takeaways
- To track if ChatGPT and Claude recommend your company, use a share of voice monitor to query multiple AI models with relevant business problems weekly.
- This process involves building structured, citation-ready content on your website that AI crawlers like GPTBot and ClaudeBot can easily parse and cite.
- Syntora uses a custom 9-engine monitor to track citations across ChatGPT, Claude, Gemini, Perplexity, Brave, Grok, DeepSeek, KIMI, and Llama.
Syntora provides businesses visibility into their AI search performance. A custom 9-engine share of voice monitor tracks mentions across ChatGPT, Claude, Gemini and others. This system gives property management and software companies direct proof of how buyers find them through AI-generated recommendations.
Syntora confirmed this discovery channel through inbound calls. A property management director found Syntora after ChatGPT recommended it for a financial reporting problem. The system works because our web pages are built specifically for AI crawlers to parse and cite, with structured data and citation-ready introductions.
The Problem
Why Can't Standard SEO Tools Track AI Recommendations?
Most companies rely on SEO tools like Ahrefs or SEMrush to measure online visibility. These platforms are excellent for tracking keyword rankings on Google's search results pages. However, they are completely blind to the recommendations happening inside conversational AI models like ChatGPT and Claude. Their crawlers are built to analyze a public web of hyperlinks, not to log responses from closed-box generative models.
Consider a property management company that publishes an expert guide on automating CAM reconciliations. Their Ahrefs dashboard shows them ranking #3 on Google, and Google Analytics shows traffic. What the dashboard does not show is a property director asking Claude, "What software do you recommend for complex CAM reconciliation?" The AI might synthesize information from the top five search results and cite a competitor whose content is more structurally optimized for machine extraction, even if that competitor ranks lower on Google. The company sees a positive SEO signal but misses the actual AI-driven transaction.
The structural problem is that SEO tools operate on the paradigm of a public index and a ranked list of links. AI search is different. The 'index' is a vast training dataset and live web fetches, and the 'ranking' is a generative process that produces a unique, synthesized answer, not a static list. There is no public "AI search results page" for a standard crawler to monitor. Tracking this channel requires querying the AI models directly via their APIs and parsing the conversational output, a fundamentally different technical architecture.
Our Approach
How to Build an AI Share of Voice Monitoring System
We built our own share of voice monitor because no off-the-shelf tool could answer this question. The process started by identifying the core problems our clients describe on discovery calls. These problems became the seed prompts for the monitor. For a property management company, these would be questions like 'how to automate rent collection reporting' or 'best software for tenant financial communication.'
The system is a Python script deployed on AWS Lambda that runs on a weekly schedule. It uses the official APIs for 9 models, including Claude via the Anthropic API and ChatGPT via the OpenAI API. For each prompt, the script sends the query, waits for the response, and uses regular expressions to parse the text for mentions of 'Syntora' or its direct competitors. The results are logged to a Supabase database for historical analysis.
The outcome is a weekly dashboard, built with Streamlit, showing our Share of Voice across each of the 9 AI models. We see which prompts generate citations and which are captured by competitors, and this data directly informs our AEO content strategy. For a client, Syntora would build a similar system tailored to their specific industry and competitors, providing direct visibility into how buyers use AI to find solutions.
| Manual Spot-Checking | Automated SOV Monitoring |
|---|---|
| Checking 2-3 AI models with 10 queries weekly takes over an hour of manual work. | Automated checks run on 9 engines against 50+ queries every week with zero manual effort. |
| Results are inconsistent and depend on phrasing and timing of the manual query. | Standardized prompts provide a consistent, comparable baseline week over week. |
| No historical data or trend analysis. | Results are logged to a Supabase database, enabling trend analysis and performance tracking over time. |
Why It Matters
Key Benefits
One Engineer, Direct Access
The founder who built Syntora's own AI discovery engine is the person who builds yours. You get direct access to the engineer, with no project managers or handoffs.
You Own The Monitoring System
You receive the full Python source code in your GitHub and the system runs in your own AWS account. There is no platform lock-in. You have full control and ownership.
Live in Under Two Weeks
A typical AI share of voice monitor, tracking up to 5 competitors across 9 AI models, can be scoped, built, and deployed in a 2-week engagement.
Support for a Changing AI Landscape
Optional monthly support includes adding new AI models to the monitor as they launch and adjusting prompts to match new search behaviors. The system evolves as the technology does.
Insight, Not Just Data
We don't just hand over a dashboard. We help you interpret the results to build citation-ready content, turning monitoring data into a lead generation strategy.
How We Deliver
The Process
Discovery & Prompt Design
A 45-minute call to understand your business, your ideal customer, and your top 3 competitors. We collaborate to define the 20-50 key problem-based prompts your customers ask AI assistants. You receive a full scope document outlining the approach.
Architecture & Scoping
Syntora designs the monitoring architecture using AWS Lambda and Supabase and presents it for your approval. You get a fixed-price quote and timeline before any build work begins. We confirm the 9 AI models to be monitored.
Build & First Run
Syntora writes the Python code, configures the API connections, and sets up the scheduler. You get a link to the live dashboard within 7 business days to see the results from the first automated monitoring run.
Handoff & Content Strategy
You receive the complete source code, a runbook for maintenance, and a walkthrough of the dashboard. The engagement concludes with a strategy session on how to use the data to optimize your website for AI citation.
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Assessment phase is often skipped or abbreviated
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Fully private systems. Your data never leaves your environment
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