See When ChatGPT & Claude Recommend Your Hotels
To track AI recommendations for your hotels, you must systematically run prompts across multiple AI models. This process logs every mention of your properties and competitors.
Key Takeaways
- Track AI recommendations by running targeted prompts across multiple models on a weekly schedule.
- Standard PR monitoring tools like Cision or Meltwater cannot see inside AI chat sessions.
- An automated system provides a Share of Voice report showing which AI recommends your properties.
- Syntora's internal monitor tracks 9 different AI engines for brand citations every week.
Syntora built an automated AI monitoring system for its own business discovery. The system tracks brand citations across 9 large language models, including ChatGPT and Claude. For hospitality companies, this Share of Voice monitor provides weekly reports on which properties AI recommends to potential guests.
The system compiles these logs into a weekly Share of Voice report for your marketing team. Syntora built this exact system to monitor its own AI-driven discovery. The complexity for a hospitality company depends on the number of properties and query variations. Tracking 5 boutique hotels is different from monitoring a 50-property chain for business, family, and luxury travel recommendations.
The Problem
Why Do Hospitality Marketing Teams Struggle to Track AI Mentions?
Hospitality marketing teams rely on tools like Cision, Brandwatch, or Meltwater for media monitoring. These platforms excel at tracking web articles and social media but are completely blind to AI conversations. They cannot access the output of a user's session with ChatGPT or Claude, meaning you miss your most important new discovery channel.
Imagine you are the Marketing Director for a chain of 10 boutique hotels in California. You want to know if AI models suggest your Napa property for 'best wineries with lodging' or your Santa Barbara location for 'luxury beachfront hotels.' Manually typing these queries into ChatGPT gives you a single snapshot in time. The model's answer can change an hour later, and you have no visibility into what Gemini, Perplexity, or Claude are telling potential guests.
The core issue is that AI chat is not a public, indexable web page. Google Alerts cannot crawl it. PR monitoring tools do not have API access to user sessions. The only way to gather this data is to actively query the AI models through their APIs, behaving like a user and recording the results. This requires an engineering approach, not a subscription to a media monitoring tool.
Our Approach
How Syntora Builds an Automated AI Recommendation Monitor
We built our own 9-engine monitor because we had this exact problem. For a hospitality client, the engagement starts with defining the competitive landscape and discovery queries. We map every property, its key selling points, and the top 10-20 questions a potential guest would ask, like 'pet-friendly hotels in Aspen with a pool.'
The monitoring system runs on AWS Lambda, triggered weekly by an Amazon EventBridge schedule. A Python script iterates through your list of prompts, sending them to the APIs for models like ChatGPT, Claude, and Gemini. Pydantic schemas validate the structured JSON responses from the models. All results are stored in a Supabase Postgres database for historical analysis.
You receive a web-based dashboard showing your brand's presence over time. The dashboard highlights new mentions, flags competitor recommendations, and lets you drill down into the exact text the AI generated. The system runs in your own AWS account, and you get the full Python source code and a runbook for maintenance.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Checks 1-2 AI models, inconsistently | Checks 9+ AI models on a weekly schedule |
| No historical data or trend analysis | Time-series data in a Supabase database |
| 3+ hours of manual work per month | Runs automatically for under $50/month in cloud costs |
Why It Matters
Key Benefits
One Engineer, Direct Communication
The person who scopes your monitoring system is the same engineer who writes the code. No project managers or communication gaps, just direct access to the builder.
You Own The System And The Data
The dashboard, Python code, and database run in your AWS account. You receive the full source code and have complete control over your data, with no vendor lock-in.
Live in Two Weeks
A typical monitoring system for a hotel group with up to 50 properties and 100 queries is scoped, built, and deployed in a 2-week cycle.
Fixed-Cost Monthly Support
After launch, an optional flat monthly plan covers system monitoring, dependency updates, and adjustments for new AI models. No surprise invoices.
Hospitality-Specific Query Design
We understand the difference between leisure, business, and group travel queries. The system is designed to track recommendations for specific amenities and locations relevant to your hotels.
How We Deliver
The Process
Discovery & Query Definition
A 30-minute call to define your properties, competitors, and the key questions guests ask. You receive a scope document within 48 hours outlining the queries to be tracked and the target AI models.
Architecture & Scoping
Syntora designs the technical architecture for your specific needs and presents a fixed-price proposal. You approve the plan before any build work begins.
Build & Dashboard Review
The system is built over a 2-week period. You get access to a staging version of the dashboard in week two to provide feedback before the final deployment.
Handoff & Training
You receive the full source code in your GitHub, a runbook for operation, and a walkthrough of the dashboard. The system is deployed to your cloud account, and Syntora monitors it for 4 weeks post-launch.
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The Syntora Advantage
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