Track Your Brand's Mentions in AI Chat
You track if AI recommends your company by querying engines like ChatGPT and Claude with problem-based prompts. The responses are logged to see when your brand is cited as a solution for those problems.
Key Takeaways
- Track AI recommendations by running targeted, problem-based prompts across multiple LLMs and logging the responses systematically.
- Manual spot-checking is unreliable as AI responses vary based on conversation history and frequent model updates.
- An automated system can query 9+ AI engines weekly to build a share-of-voice report for your brand and competitors.
- Syntora's internal monitor uses Python scripts to run over 200 prompts against engines like ChatGPT and Claude.
Syntora built an internal AI citation monitor to track its own recommendations across 9 LLMs, including ChatGPT and Claude. The system proves how structured, industry-specific content drives qualified leads from AI search. This monitoring provides direct, weekly evidence of Answer Engine Optimization (AEO) performance.
Syntora validated this exact method when prospects found us after ChatGPT recommended Syntora for their specific business problems. We built our own 9-engine monitoring system to track these citations weekly. The system proves the direct link between our structured content and AI-driven business discovery.
The Problem
Why Can't Ecommerce Brands Just Check ChatGPT Manually?
Many Ecommerce brands start with manual spot-checks. A marketing manager asks ChatGPT for the “best sustainable bedding” and screenshots the result if their brand appears. This approach fails because AI responses are not static like search results. They change based on phrasing, conversation history, and the underlying model version you happen to access, providing a snapshot of just one of millions of possible answers.
In practice, this creates false confidence or confusion. An Ecommerce director for a DTC coffee brand asks Claude “best fair trade espresso beans” and sees her product listed. The next day, her colleague asks the same question and gets a completely different list. This inconsistency makes manual tracking useless for performance measurement. You have no reliable way to prove if your content strategy is working.
The structural problem is that LLMs are not public web pages. Standard monitoring tools like Google Alerts or Brand24 are built to scrape public websites and social media feeds. They have no access to the output of private, session-based AI conversations. There is no off-the-shelf tool that can systematically query these closed systems, log the varied responses, and analyze them for trends.
Without a system, you are blind to a critical new customer acquisition channel. Competitors optimizing their content for AI citation are getting recommended to buyers at the exact moment of need. Your brand remains invisible, and you miss out on direct proof of how modern buyers use AI to discover and purchase products.
Our Approach
How to Build an Automated AI Share-of-Voice Monitor
The first step is to define the discovery prompts for your brand. These are not SEO keywords, but problem and solution queries your ideal customers use. For an Ecommerce company, this could range from “what is the most durable luggage for international travel” to “where can I buy non-toxic cleaning supplies online”. Syntora helps map 50 to 100 of these critical discovery questions.
We built our own monitor using Python and the official APIs for models from OpenAI, Anthropic, and Google. A scheduled script runs each prompt against 9 different AI engines weekly, including ChatGPT, Claude, Gemini, and Perplexity. The system uses httpx for asynchronous requests to run hundreds of queries in parallel efficiently. All responses are parsed and stored in a Supabase database for analysis.
For your Ecommerce company, a similar system would provide a dashboard showing your citation share of voice over time. You would see which prompts trigger a recommendation for your brand versus key competitors. The data would clearly show if a new blog post on “bamboo fabric benefits” resulted in more citations from AI, providing direct ROI on your content efforts. The entire system runs on AWS Lambda for under $50 per month.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Queries 1-2 AI models inconsistently | Queries 9+ AI models weekly on a fixed schedule |
| Captures a single, non-repeatable answer | Logs thousands of responses to build a trend line |
| Provides anecdotal evidence with zero metrics | Generates a share-of-voice report with competitor tracking |
Why It Matters
Key Benefits
One Engineer From Call to Code
The person who built Syntora's own monitoring system is the one who builds yours. No handoffs, no project managers, no communication gaps.
You Own All the Code
You get the full Python source code in your GitHub repository and a maintenance runbook. There is no vendor lock-in or recurring license fee.
Realistic 3-Week Build
An AI citation monitoring system is a well-defined project. Expect a discovery and prompt-mapping week, a build week, and a deployment and testing week.
Defined Post-Launch Support
Optional monthly maintenance covers keeping API connections updated as models change, monitoring for errors, and helping you refine your prompt list.
Based on Proven Experience
This is not a theoretical build. Syntora uses this system for its own lead generation and has direct proof from discovery calls that it works.
How We Deliver
The Process
Discovery & Prompt Mapping
A 45-minute call to understand your products, competitors, and customer problems. We collaborate on a list of 50-100 prompts that represent how buyers would search for you using AI.
Architecture & Scoping
You receive a scope document detailing the 9 target AI engines, the data schema for logging results, and the dashboard layout. You approve the plan before any code is written.
Build & Weekly Demos
Syntora builds the Python scripts and Supabase backend. You get a weekly live demo to see the system querying AIs and populating the database with real results.
Handoff & Training
You receive the full source code, deployment on your AWS account, a runbook for maintenance, and a training session on how to interpret the share-of-voice dashboard.
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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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