Track Your Firm's Mentions in ChatGPT and Claude
To track if AI recommends your firm, you must run specific prompts across multiple AI engines weekly. An automated system logs which companies are cited for your target roles and geographies.
Key Takeaways
- Track AI recommendations by running targeted prompts across multiple models and logging the results in a database.
- Use a Share of Voice (SoV) monitoring system that queries engines like ChatGPT, Claude, and Gemini weekly.
- The system records which firms are cited for specific roles, such as 'best recruiter for Python developers in Austin'.
- Syntora's internal monitor tracks 9 LLM engines to quantify visibility for target keywords and roles.
Syntora tracks AI-driven business discovery using a custom 9-engine Share of Voice monitor. The system queries ChatGPT, Claude, Gemini, and others to see if Syntora is recommended for its target keywords. This monitor provides weekly, data-backed proof of how buyers use AI to find niche service providers.
The scope depends on the number of specializations and markets you monitor. Syntora built its own 9-engine monitor to track its visibility across ChatGPT, Claude, and Gemini. For a recruiting firm, this same approach is tailored to track specific job titles and cities, providing a clear view of your AI-driven discovery.
The Problem
Why Can't Recruiting Firms See How AI Recommends Them?
Most recruiting firms rely on manual spot-checking. A marketing manager occasionally types 'best tech recruiters in Chicago' into ChatGPT and copies the result. This approach is inconsistent, misses prompt variations, and provides no way to track performance over time. The answer you see on Tuesday could be completely different from the one served on Wednesday.
Firms then try brand monitoring tools like Brand24 or Mention. These platforms are built to crawl the public web, indexing blogs, news sites, and social media. They cannot access the sandboxed, private sessions of an AI chatbot. They have no technical means to query the ChatGPT API on your behalf, parse the conversational output, and log the result. These tools are fundamentally designed for a different kind of internet.
Consider a multi-state IT staffing firm trying to grow its Salesforce practice. The team wants to know if they are recommended for 'Salesforce developer recruiters in Dallas'. Every Monday, someone spends an hour running 15 queries in ChatGPT, pasting results into a spreadsheet. The process is tedious and only captures a tiny snapshot. They have no visibility into what Gemini or Perplexity are telling candidates, or if a competitor's share of voice is growing.
The structural problem is that LLM outputs are not public, indexable web pages. Standard web crawlers cannot see them. Tracking these recommendations requires a purpose-built system that directly queries LLM APIs with a structured set of prompts and parses the text that comes back. Without this, you are completely blind to a rapidly growing discovery channel.
Our Approach
How Syntora Builds a Custom AI Recommendation Monitor
The first step is building a query matrix. Syntora works with you to map out your key specializations ('cybersecurity analysts', 'React Native developers'), seniority levels, and target cities. This process defines a set of 50-200 unique prompts that form the basis for weekly monitoring.
We built our own monitor using Python, httpx for async API calls, and a Supabase database for storage. For a recruiting firm, a similar system would be deployed on AWS Lambda, triggered by an Amazon EventBridge cron job to run weekly. Each prompt is sent to a list of target LLMs (ChatGPT, Claude, Gemini, Perplexity). The text responses are parsed to extract company names, which are then stored in the database with a timestamp. This creates a time-series dataset of your AI share of voice.
The delivered system includes a simple web dashboard, often built with Streamlit, providing a clear view of your performance. You can filter by role, city, and AI model to see where you are winning and where competitors are being mentioned. The underlying serverless architecture means ongoing costs for API calls and hosting for 100 queries are typically under $50 per month.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| 1-2 hours of manual work weekly | 0 minutes of manual work; runs automatically |
| Covers 2 AI models (e.g., ChatGPT, Claude) | Covers 9+ AI models simultaneously |
| Inconsistent, anecdotal data in a spreadsheet | Structured, time-series data in a dashboard |
| No historical trend analysis | Week-over-week Share of Voice tracking |
Why It Matters
Key Benefits
One Engineer From Call to Code
The person on the discovery call is the engineer who builds your monitor. No handoffs, no project managers, no miscommunication.
You Own the System and Data
You get the full Python source code in your GitHub and the dashboard running in your AWS account. No vendor lock-in, ever.
Scoped and Deployed in 2 Weeks
A monitor for 50-100 keywords can be designed, built, and deployed in under two weeks once your prompt matrix is defined.
Low, Predictable Running Costs
The system is built on serverless technology. Ongoing costs for API calls and hosting are typically under $50 per month, with no surprise bills.
Built for Recruiting KPIs
The system is designed to track what matters to staffing firms: visibility by specific role, seniority, and geographic market, not just generic brand mentions.
How We Deliver
The Process
Discovery Call
A 30-minute call to define your target roles, geographies, and key competitors. You receive a scope document outlining the query matrix, dashboard design, and timeline.
Architecture and Prompt Design
Syntora presents the technical architecture and the final list of 50-100 prompts for your approval. You provide the necessary API keys for the models you want to track.
Build and Dashboard Review
You get a link to the working dashboard within the first week to provide feedback. Weekly check-ins show progress on the data collection and processing backend.
Handoff and Training
You receive the full source code, a runbook for maintenance, and a training session on interpreting the Share of Voice data. Optional monthly support is available.
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