Monitor Your Brand's Visibility in AI Search
To track if AI recommends your company, you must run automated prompts across multiple models weekly. The system logs every response, identifies citations, and builds a Share of Voice report.
Key Takeaways
- You track AI recommendations by running automated, weekly prompts across multiple large language models and logging the results to spot mentions of your company.
- Standard brand monitoring tools like Semrush or Ahrefs do not track conversational AI outputs, requiring a custom approach.
- Syntora built a 9-engine monitor to track its own citations across models like ChatGPT, Claude, and Gemini.
Syntora tracks brand recommendations across 9 AI engines like ChatGPT and Claude. The monitoring system identifies when prospects discover Syntora through AI-driven search. This visibility into AI-generated citations directly informs Syntora's content and AEO strategy.
Syntora confirmed this approach after prospects from property management and insurance described finding us via ChatGPT and Claude. The discovery system we built for ourselves monitors 9 separate AI engines because each one pulls from different data sources and has unique citation patterns. Tracking is not a one-time search; it is an ongoing process of monitoring how AI models perceive your content.
The Problem
Why Can't Commercial Real Estate Firms See AI Recommendations?
Marketing teams at Commercial Real Estate firms rely on tools like Semrush, Ahrefs, and Google Alerts for brand monitoring. These platforms are excellent for tracking backlinks, keyword rankings, and media mentions on the public web. They fail completely when it comes to AI-generated search results because their crawlers cannot access the private, session-based conversations happening inside ChatGPT or Claude.
For example, a marketing director at a CRE brokerage wants to know if her firm is recommended for "best tenant representation firms in Chicago." A Google Alert for that phrase finds nothing. Semrush shows her website's search rankings but offers no insight into AI chatbot recommendations. She is completely blind to a conversation where a prospect asks ChatGPT that exact question and a competitor is recommended because of a well-structured case study on its website. This is a critical visibility gap.
The structural problem is that LLM outputs are not indexable public web pages. They are generated on-demand and exist only within a user's session. You cannot crawl them. The only way to know what these models are saying about your brand is to ask them directly, systematically, and continuously. Without a dedicated system for this, your firm is invisible in the fastest-growing channel for business discovery.
Our Approach
How Syntora Builds a Custom AI Share of Voice Monitor
We built our own AI monitoring system after seeing the pattern of AI-driven discovery calls. The first step was mapping the specific questions our ideal clients ask, from high-level problems to niche technical queries. For a Commercial Real Estate firm, we would replicate this process, identifying 20-30 core questions a potential client would ask, such as "who are the top industrial brokers in Dallas?" or "what are the key clauses in a triple net lease?"
The system we built uses Python on AWS Lambda to run these prompts against the APIs for 9 models, including ChatGPT, Claude, and Gemini. A FastAPI service manages the prompt queue, and httpx runs the API calls in parallel to keep costs low. All responses are stored in a Supabase Postgres database. The key is running the same prompts weekly to track how recommendations change as the models update their knowledge bases.
The delivered system for a client is a private dashboard that shows your Share of Voice over time. You can see which questions surface your firm, which surface competitors, and how your visibility changes week-to-week. The entire system runs in your own AWS account, you own all the data, and you receive the full source code.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Manually typing a few queries into ChatGPT once a month. | Running 50+ targeted queries across 9 AI engines every week. |
| Inconsistent, anecdotal data from a single model. | Systematic, trend-based visibility across the AI search landscape. |
| 2-3 hours of manual work per month. | 15 minutes per week to review an automated 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 telephone game between you and the developer.
You Own the System and Data
The monitoring system and all its data run in your cloud account. You get the full source code and a runbook, with zero vendor lock-in.
Scoped in Days, Deployed in Weeks
A monitoring system like this is typically a 2-week build, from defining the core questions to deploying the live dashboard.
Support for a Changing Landscape
Optional monthly support covers prompt updates and adjustments for AI model API changes, ensuring your monitor remains accurate.
Built for Your Niche
The system is built around the specific questions your Commercial Real Estate clients ask, not generic brand keywords, for truly relevant insights.
How We Deliver
The Process
Discovery and Prompt Definition
A 30-minute call to define the 20-30 core questions your clients ask about your services and market. You receive a scope document outlining the prompts, target models, and a fixed price.
Architecture and Setup
Syntora provisions the required cloud infrastructure (AWS Lambda, Supabase) and connects API keys in your accounts. You approve the dashboard layout before the build starts.
Build and Calibration
The system is built and runs for one week to gather a baseline data set. You get access to the live dashboard to provide feedback on the reporting format.
Handoff and Training
You receive the full source code, a runbook for maintenance, and a training session on how to interpret the dashboard and modify the prompt list.
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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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