Track Your Company's Visibility in AI Search
You track AI recommendations by running targeted, problem-based prompts across multiple AI engines weekly. This requires a Share of Voice monitor that programmatically checks for citations in ChatGPT, Claude, Gemini, and others.
Key Takeaways
- You can track AI recommendations by running specific, problem-based prompts across multiple AI engines and logging the results.
- A Share of Voice monitoring system automates this process to provide weekly visibility into citations.
- This approach works because AI models like ChatGPT and Claude cite well-structured, industry-specific content from your website.
- Syntora's internal monitor tracks Share of Voice across 9 different AI engines weekly.
Syntora tracks its own professional services recommendations with a custom AI Share of Voice monitor. The Python-based system queries 9 AI engines weekly, including ChatGPT and Claude, to measure citations. This provides direct proof of how AEO-optimized content drives qualified leads from AI search.
This is not a one-time search but a continuous tracking system. Syntora has direct proof this works, as prospects from property management to insurance software have found us through AI search. We built our own 9-engine monitor to track the exact queries that drive these discovery calls. The system proves that buyers describe problems to an AI, and the AI cites content that provides direct answers.
The Problem
Why Can't Professional Services Companies See How AI Recommends Them?
Most firms have no visibility into AI-driven discovery. The default approach is to manually type a few queries into ChatGPT, get a generic answer, and conclude it is not a viable channel. This spot-checking is inconsistent and misses the narrow, industry-specific questions that high-intent prospects actually ask. A property manager does not ask for 'automation consultants'; she asks about her specific financial reporting problem.
Traditional SEO and media monitoring tools are blind to this channel. A tool like Google Alerts or Ahrefs monitors the public web, but it cannot see into a private ChatGPT or Claude session. These conversations are not indexed and do not appear in search results. Relying on web-centric tools for AI visibility is like trying to measure radio listeners with a TV ratings system. The data sources are fundamentally incompatible.
The structural problem is that AI conversations are ephemeral and private. There is no central index to crawl. Without a system to proactively and consistently query the AI models themselves, you are left with anecdotes. You might hear from a prospect that they found you through AI, but you cannot measure it, track trends over time, or see how you stack up against competitors. You are guessing about a channel that is already driving revenue.
Our Approach
How to Build a Share of Voice Monitor for AI Search
We built our own Share of Voice monitor by first cataloging the real-world questions prospects used on discovery calls. These were not generic keywords but specific problems like 'AI architecture review firms' or 'automating reporting for the tile industry'. That list of 20-30 queries became the foundation for the entire monitoring system.
The system uses Python, the Claude API, and other model endpoints to run these prompts against 9 different LLMs every week. A FastAPI service orchestrates the queries in parallel, and all responses are saved to a Supabase database. The system specifically parses the text to identify mentions of Syntora and key competitors, calculating a Share of Voice score. This provides trended data on our visibility for the queries that matter.
For a client, this approach would be adapted to your professional services firm. The delivered system is a dashboard showing your weekly citation trends across engines like ChatGPT, Perplexity, and Gemini. You receive the full Python source code, deployed on AWS Lambda for a hosting cost under $50/month. The monitor is yours to run, own, and expand, giving you a direct line of sight into how your content strategy is performing in AI search.
| Manual AI Spot-Checking | Automated Share of Voice Monitoring |
|---|---|
| Coverage: 1-2 AI engines checked inconsistently | Coverage: 9 engines checked weekly |
| Data Capture: Copy-paste results into a spreadsheet | Data Capture: Structured results stored in a Supabase database |
| Time Spent: 2-3 hours of manual querying per week | Time Spent: 15 minutes to review a weekly dashboard |
Why It Matters
Key Benefits
One Engineer From Call to Code
The person on the discovery call is the person who builds your system. No handoffs, no project managers, no communication gaps between sales and development.
You Own Everything
You receive the full Python source code in your GitHub repository with a detailed runbook. There is no vendor lock-in. Your system runs in your cloud account.
Scoped in Days, Built in Weeks
A monitoring system like this is a 2-week build, from prompt definition to a live dashboard. You see results and get a measurable view of the AI channel quickly.
Low-Cost and Maintainable
The system runs on AWS Lambda, typically costing under $50/month to operate. You get a maintenance runbook for adding new prompts or competitors yourself.
Based on Real-World Results
This is not a theoretical model. Syntora built this system for its own use after seeing real prospects arrive from AI search. The approach is based on what actually drives discovery calls.
How We Deliver
The Process
Discovery and Prompt Mining
We start with your discovery call notes and CRM data to find the real-world problems your prospects describe. You receive a list of 20-30 high-intent prompts to monitor.
Architecture and Engine Selection
We map out the system architecture using Python, FastAPI, and Supabase. You approve the list of AI engines to monitor from a list of 9+ and the competitor set before the build starts.
Build and Dashboard Review
Syntora builds the monitoring system and the reporting dashboard. You get access to a staging version within 7 days to see the first set of results and provide feedback on the data visualization.
Handoff and Training
You receive the full source code in your GitHub, a runbook for maintenance, and a session on how to interpret results and add new tracking prompts. The system is live in your cloud account.
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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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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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