Track AI Search Citations for Your Healthcare Practice
To track AI recommendations, you monitor engines like ChatGPT and Claude weekly with specific prompts. This process reveals when your company is cited as a solution.
Key Takeaways
- You track AI recommendations by querying models like ChatGPT and Claude weekly with specific prompts.
- The system logs responses from multiple AI engines to measure your "Share of Voice" against competitors.
- This reveals which of your web pages AI crawlers are using for citations and content generation.
- Syntora's internal system monitors 9 large language models to track our own AI-driven discovery.
Syntora tracks its own business discovery by monitoring 9 large language models, including ChatGPT and Claude. This AI Share of Voice system identified that structured, citation-ready content directly leads to new client discovery calls. For a Healthcare company, this same approach provides direct visibility into how AI recommends their services to patients.
The system uses a Share of Voice monitor to log responses from up to 9 different AI models, comparing your mentions against competitors. For our own marketing, Syntora built a monitor that queries LLMs with 50 different prompts every week. A similar system for a healthcare practice would track specific service-related queries like "best orthopedic surgeons in [city]" or "how to choose a physical therapist."
The Problem
Why Can't Standard SEO Tools Track AI Mentions in Healthcare?
Healthcare marketing teams rely on tools like Semrush and Ahrefs for SEO. These tools are built to track rankings on Google's list of links. They cannot see inside the conversational black box of ChatGPT, Claude, or Perplexity. They report on keyword positions, not conversational citations. You might rank #1 on Google for "hip replacement recovery," but you have zero visibility if ChatGPT recommends a competitor's blog post when a user asks that question.
Consider a multi-location dental practice that has invested heavily in content about "sedation dentistry." A potential patient, anxious about a procedure, asks Claude: "which dentists in San Diego offer painless root canals?" Claude synthesizes information from various websites. If your site has a well-structured page on the topic, it might get cited. But if a competitor's page has a semantic HTML table comparing sedation options, the AI is more likely to use that. Your Semrush report shows you ranking well, but you're blind to the fact that you're losing high-intent prospects in AI conversations.
The fundamental issue is architectural. SEO tools scrape public search engine results pages (SERPs). AI chat interfaces do not have a public, crawlable SERP. Each answer is generated on-demand and can vary between sessions. To track mentions, you must directly query the AI models via their APIs, log the responses, and parse them for your brand name. Standard SEO platforms are not built for this interactive, API-driven monitoring.
Our Approach
How Syntora Builds a Custom AI Share of Voice Monitor
The process starts by defining your "keyword-to-competitor" matrix. We identify the top 10-15 questions potential patients ask about your core services and list your top 5 direct competitors. This discovery phase results in a clear monitoring plan that defines the exact prompts the system will run weekly.
Syntora builds a system using Python and AWS Lambda to run these prompts against the APIs for 9 LLMs, including ChatGPT, Claude, and Gemini. A scheduled job runs weekly, storing the full text response in a Supabase database. We use libraries like httpx for parallel API calls to keep the run time under 5 minutes. The serverless architecture choice keeps monthly hosting costs under $20.
The delivered system is a dashboard that shows your Share of Voice percentage over time. You can see which AI models mention you most, which questions generate citations for your competitors, and which of your website pages are being referenced. This gives your content team direct data on how to create content that AI models are more likely to cite, a method we proved with our own lead generation.
| Manual Spot-Checking | Automated Share of Voice Monitoring |
|---|---|
| Checking 2-3 AIs with a few queries | Systematically querying 9 AIs with 50+ prompts |
| No historical data or trend analysis | Weekly trend data stored in a database |
| Hours of manual work for incomplete data | Fully automated 5-minute process |
Why It Matters
Key Benefits
One Engineer Builds Your System
The person you talk to on the discovery call is the engineer who writes the code. There are no project managers or handoffs, ensuring your exact requirements are built.
You Own All the Code and Data
You get the full Python source code in your GitHub repository and the data in your own database. There is no vendor lock-in. You are free to extend or maintain the system yourself.
A 2-Week Build Timeline
For a standard monitor tracking 5 competitors and 20 prompts, the system is typically designed, built, and deployed within two weeks of the discovery call.
Clear Support After Deployment
Syntora monitors the system for 4 weeks post-launch to ensure stability. After that, an optional flat monthly retainer is available for maintenance, updates, and prompt adjustments.
Built From Real-World Experience
This is not a theoretical product. Syntora uses this exact system to track its own AI-driven leads, proving the value of structured content and direct monitoring.
How We Deliver
The Process
Discovery & Prompt Design
A 30-minute call to define your key services, competitors, and the patient questions you want to track. You receive a scope document with the full prompt list and a fixed price within 48 hours.
Architecture & API Setup
Syntora outlines the technical architecture using AWS Lambda and Supabase. You provide API keys for the AI models you want to monitor, and we review the plan before any code is written.
Build & Dashboard Review
The system is built over one week, with a check-in to review the first set of results. We refine the dashboard with you to ensure it delivers the insights your marketing team needs.
Handoff & Training
You receive the complete source code, a runbook for maintenance, and a walkthrough of the dashboard. Syntora provides support to ensure your team can interpret the data and take action.
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The Syntora Advantage
Not all AI partners are built the same.
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Assessment phase is often skipped or abbreviated
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We assess your business before we build anything
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Typically built on shared, third-party platforms
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Fully private systems. Your data never leaves your environment
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May require new software purchases or migrations
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Zero disruption to your existing tools and workflows
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Training and ongoing support are usually extra
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Full training included. Your team hits the ground running from day one
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Code and data often stay on the vendor's platform
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You own everything we build. The systems, the data, all of it. No lock-in
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