Track Your Home Services Brand Mentions in ChatGPT and Claude
You track AI recommendations by running targeted prompts across multiple AI models and logging their responses over time. This process uses a monitoring system to check for citations, brand mentions, and competitor placements weekly.
Key Takeaways
- You track AI recommendations by running targeted prompts across multiple AI models and logging their responses over time.
- This process replaces manual spot-checks with a systematic approach to measure your visibility in AI-generated answers.
- A custom monitoring system can track your brand, competitors, and service-specific queries across different AI platforms.
- Syntora's internal system monitors brand citations weekly across 9 different AI engines to measure AI-driven discovery.
Syntora tracks AI recommendations for its own business using a custom 9-engine Share of Voice monitor. The system queries ChatGPT, Claude, and Gemini weekly to log brand citations and competitor mentions. This provides direct proof of how AI search drives business discovery.
Syntora built this exact system to track its own visibility after prospects reported finding the company through ChatGPT and Claude. The system monitors 9 AI engines, providing direct evidence of how buyers use AI to discover solutions. For a home services company, this means tracking not just your brand name but also service queries like "best HVAC repair in Dallas" to see who AI models recommend.
The Problem
Why Can't Standard SEO Tools Track AI Recommendations for Home Services?
Home services companies rely on local visibility, but the tools used for Google are blind to AI chat. Your marketing team might use Semrush or Ahrefs to track keyword rankings on a search engine results page. These tools cannot log into ChatGPT, run a conversational query like "who is the most reliable plumber in Austin?", and record the answer. They are built to analyze static HTML, not the output of a generative model.
This leaves you with manual spot-checking. The owner asks someone to check what ChatGPT says. They run a few searches, get different answers each time, and come back with a shrug. There is no way to know if your content marketing is working or if a competitor is suddenly getting all the AI-driven recommendations. The process is not repeatable, not scalable, and provides zero historical data for comparison.
For example, a marketing manager for a multi-location roofing company might test five different prompts about storm damage repair. One day, ChatGPT recommends their business; the next day, it recommends three competitors. Without a systematic log, it is impossible to know if this was a random fluctuation or a meaningful shift in visibility. You cannot build a strategy on anecdotal evidence.
The structural problem is that AI models are non-deterministic and conversational. Their answers depend on chat history and subtle changes in the prompt. Standard SEO tools are built for a world of stable, ranked URLs. They are architecturally incapable of tracking performance inside a closed, conversational interface. You need a system built to interact with these models via API and store their responses over time.
Our Approach
How Syntora Builds a Custom AI Share of Voice Monitor
Syntora first used this approach for its own marketing. We built a system to prove how prospects were finding us through AI. For a home services client, the process would start with defining the exact queries that matter to your business. This includes branded searches, competitor names, and unbranded service queries for each of your geographic territories, such as "emergency electrical repair Phoenix."
We built our internal monitor using Python, AWS Lambda for scheduled execution, and Supabase for data storage. For a home services company, the system would use official APIs from OpenAI (for ChatGPT) and Anthropic (for Claude) to run your list of 50-100 core queries every week. Each AI-generated response is parsed and stored in a database, logging which companies were mentioned, in what order, and the surrounding context.
The delivered system is a simple, private dashboard. You see your AI Share of Voice week over week for each service and location. You can identify which competitors are gaining traction in AI recommendations and get direct feedback on whether your own content optimizations are influencing the models. The entire system runs on your own cloud infrastructure, giving you full ownership of the data and the code.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Inconsistent, non-repeatable queries | Systematic, scheduled prompts for consistent tracking |
| No historical data or trend analysis | Results logged in a database for week-over-week comparison |
| 1-2 hours per week of manual labor | Runs automatically for under $50/month in cloud costs |
Why It Matters
Key Benefits
One Engineer, From Call to Code
The person you speak with on the discovery call is the engineer who writes the code. No project managers, no handoffs, no miscommunication.
You Own the System and Data
You get the full Python source code in your GitHub and the data in your own database. There is no vendor lock-in and no recurring license fee.
Built in Under Two Weeks
A typical AI monitoring system for a single-location business can be designed, built, and deployed in a 2-week sprint from discovery to handoff.
Low-Cost, High-Impact Support
After launch, Syntora offers an optional monthly maintenance plan to manage API changes and monitor system health. No long-term contracts.
Focused on Local Home Services
The system is designed to handle geo-specific queries essential for home services marketing, a detail generic tracking tools miss.
How We Deliver
The Process
Discovery and Query Definition
A 30-minute call to identify your key services, top 3-5 competitors, and geographic service areas. You receive a scope document detailing the exact prompts to be monitored.
Architecture and Scoping
Syntora presents the technical architecture for the monitoring system and the dashboard layout. You approve the final query list and AI models to be tracked before the build begins.
Build and Weekly Check-ins
The system is built over a 1-2 week period. You get weekly updates and see the first set of monitoring data as soon as the core components are live to provide early feedback.
Handoff and Documentation
You receive access to the live dashboard, the complete source code in your repository, and a runbook explaining how the system works. Syntora provides support for 4 weeks post-launch.
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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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