Track Your Visibility in AI Search
You track AI recommendations by running specific prompts against LLMs like ChatGPT and Claude weekly. The system logs every citation of your brand.
Key Takeaways
- To track AI recommendations, you must regularly query models like ChatGPT and Claude with relevant prompts and log every mention of your company.
- Manual spot-checking misses inconsistent results and cannot provide a reliable share of voice metric over time.
- An automated monitoring system provides structured data for trend analysis, saving over 8 hours of manual labor per month.
- Syntora's internal system monitors 9 AI engines weekly, providing a consistent measure of AI-driven discovery.
Syntora's AI monitoring system tracks brand mentions across 9 AI engines for companies in the Education sector. The system provides weekly reports showing the exact prompts that trigger a recommendation. This gives marketing teams a direct view of their AI-driven discovery funnel.
This requires an automated script to query multiple AI engines and parse the text-based responses for mentions. Syntora built this system for its own use to monitor 9 different AI engines. The pattern is consistent: AI models find and cite structured, industry-specific content to answer user questions about business problems.
The Problem
Why Can't Education Companies Track AI Mentions with Standard SEO Tools?
Education marketing teams rely on Semrush or Ahrefs for SEO. These tools track Google rankings and backlinks perfectly, but they have zero visibility into AI chat conversations. They cannot tell you if ChatGPT recommended your coding bootcamp to a user asking about career change programs or if Claude cited your university's research in a detailed answer.
Consider an online MBA program. A prospective student asks Claude, 'what are the best part-time MBA programs for working parents with a focus on finance?' The answer is generated in real-time and is not a searchable, indexable webpage. Your analytics platform, like Google Analytics or HubSpot, will only see 'Direct' traffic if the user clicks a link, with no context on the AI-driven recommendation that sent them there.
The core problem is that AI-generated answers are ephemeral and conversational. Unlike a Google search results page, a ChatGPT session is private and personalized. There is no public-facing URL to monitor. Standard monitoring tools are built to crawl the public web, not to interact with a conversational API. They are architecturally incompatible with tracking visibility inside walled-garden AI models.
Our Approach
How Syntora Builds an AI Share of Voice Monitor for Education Companies
The first step is a prompt audit. We identify the top 50-100 questions prospective students ask about your programs, from 'best online data science certificate' to 'is [Your University] accredited for nursing?' This forms the basis of the weekly monitoring query set. The audit ensures the system tracks visibility for the questions that actually drive enrollment.
We built our own monitor using Python scripts and the Claude API, running on AWS Lambda for cost-effective execution. For an education company, the system would use httpx to make parallel API calls to ChatGPT, Claude, Gemini, and Perplexity. A Supabase database would store every response, and a simple string-matching algorithm flags any mention of your brand or key programs. The system runs on a weekly cron schedule.
The result is a weekly report delivered to your inbox. The report shows which AI models mentioned your company, the exact prompts that triggered the recommendation, and the full context of the answer. This gives you a direct, measurable view of your Share of Voice in AI search. You also get a historical dashboard built in Metabase to track trends over time.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Coverage: 2-3 queries on 1-2 AI models per week | Coverage: 50+ queries across 9 AI models weekly |
| Data Logging: Copy-pasting results into a spreadsheet | Data Logging: Structured data stored in a Supabase database |
| Time Spent: 2 hours of manual work per week | Time Spent: 0 hours, fully automated report generation |
Why It Matters
Key Benefits
One Engineer From Call to Code
The person on the discovery call is the person who builds the monitoring system. No handoffs, no project managers, no telephone game between you and the developer.
You Own the System and All Data
You get the full source code in your GitHub repo with a maintenance runbook. The citation data is yours, stored in a database you control, with no ongoing license fees.
Monitor Built in 2 Weeks
After the prompt set is finalized, the monitoring system is typically built and deployed in two weeks. You receive your first automated report at the start of week three.
Flat Support After Launch
Optional monthly maintenance covers monitoring, API updates, and bug fixes. No surprise bills. You can cancel anytime and self-manage using the provided runbook.
Education-Specific Prompt Engineering
We understand the difference between prompts for undergraduate admissions vs. executive education. Monitoring queries are tailored to your specific student personas and programs.
How We Deliver
The Process
Discovery and Prompt Audit
A 30-minute call to understand your programs, target students, and key competitors. You receive a written scope document within 48 hours with a proposed prompt list and fixed price.
Scoping and Architecture
You approve the final prompt list, competitor set, and the 9 target AI engines. Syntora presents the technical approach for your approval before any build work starts.
Build and Calibration
Weekly check-ins show progress. You see the first set of raw results to verify relevance and calibrate the matching logic before the reporting dashboard is finalized.
Handoff and Reporting
You receive the full source code, deployment runbook, and access to your monitoring dashboard. The system goes live, and you receive your first automated weekly report.
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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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Typically built on shared, third-party platforms
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Fully private systems. Your data never leaves your environment
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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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