Track Your Brand's Share of Voice in AI Search
You track AI chatbot recommendations by systematically querying multiple large language models with relevant questions. You then parse the generated responses for mentions of your brand, products, or website URLs.
Syntora helps service firms track their visibility within AI chatbot recommendations. We design custom automated systems that systematically query multiple large language models, parse responses for brand and competitor mentions, and visualize Share of Voice trends. This enables firms to understand and improve their standing in a crucial emerging channel.
This process requires an automated system that runs regular queries across several leading AI engines, including Gemini, Claude, and Perplexity. The objective is to track not only your brand but also your top competitors to measure your relative visibility, or Share of Voice (SoV), over time.
Syntora designs and builds custom monitoring solutions tailored to your specific market and competitive landscape. We'd start by defining a precise list of questions and target AI models relevant to your service firm. The complexity of the system, including the number of AI engines, query volume, and reporting requirements, would determine the typical build timeline, which often ranges from 4-8 weeks for an initial deployment. We have extensive experience building document processing pipelines using Claude API for sensitive financial documents, and the same robust pattern applies to parsing AI chatbot responses for brand mentions.
The Problem
What Problem Does This Solve?
Most teams start by manually asking questions in ChatGPT. This approach is not repeatable and provides zero historical data. Because model outputs change daily, a single screenshot of a favorable answer is useless for proving consistent visibility. This method only covers one AI engine, ignoring the seven or eight others your customers use.
Next, teams look at their existing SEO tools like SEMrush or Ahrefs. These platforms are designed to track keyword rankings and backlinks on the public web. They have no access to the outputs of closed AI systems like Claude, ChatGPT, or Grok. They cannot tell you if Perplexity cited your website as a source in an answer generated five minutes ago.
A 15-person marketing agency we worked with faced this exact issue. They needed to demonstrate ROI to their client, a regional law firm. They would periodically ask AI chatbots “who is the best personal injury lawyer in Austin?” and get different answers each time. They had no way to quantify their progress, compare visibility against rival firms, or build a business case for their content strategy.
Our Approach
How Would Syntora Approach This?
Syntora's approach begins with a comprehensive discovery phase to define the most relevant questions and target AI engines for your service firm. We would collaborate with you to mine a curated list of questions from sources like Google's People Also Ask data, Reddit threads, and industry-specific forums. This initial list would typically range from 100-200 questions, based on the scope. We would then configure secure API access for a panel of leading AI engines, which can include Gemini, Perplexity, Brave, Claude, ChatGPT, Grok, DeepSeek, KIMI, and Llama, depending on your monitoring needs.
The core of the system would be a Python-based query pipeline. This pipeline would utilize httpx for efficient asynchronous API calls, ensuring high concurrency when querying multiple AI engines in parallel. The entire process would be scheduled to run weekly, typically managed via GitHub Actions or an AWS Lambda function, providing a robust and automated execution environment. All raw responses from the AI engines would be logged into a Supabase Postgres database, establishing a permanent and auditable record of the data.
A subsequent Python script would then process these raw responses. This script uses advanced pattern matching and natural language processing techniques to accurately identify mentions of your brand name, specified competitors, and any associated URLs. It would also be designed to record the relative position of each mention within the response, noting if your firm was mentioned first, second, or third. This structured data, encompassing the mention, its position, and the source URL, would be stored in a separate results table within the Supabase database.
Finally, the delivered system would include a customized web dashboard. This dashboard would connect to the Supabase database and visualize your Share of Voice trends week over week, allowing you to monitor your brand's visibility and compare it against competitors. The dashboard would update automatically after each scheduled run, requiring no manual intervention from your team. Clients would need to provide brand names, competitor names, relevant URLs, and a list of seed keywords for question mining. The deliverables would include the deployed monitoring system, source code, documentation, and a user guide for the dashboard.
Why It Matters
Key Benefits
Get Your First Report in 7 Days
From kickoff to your first weekly Share of Voice report in one business week. Stop guessing about your AI visibility and start measuring it now.
Fixed Build, Predictable Hosting
A one-time project cost with monthly Supabase and Vercel hosting typically under $50. No per-seat licenses or recurring SaaS subscription fees.
You Own the Monitoring System
We deliver the complete Python scripts and dashboard code in your private GitHub repository. You own the asset, not just a login to a third-party tool.
Automated Weekly Runs, Zero Upkeep
The system runs automatically via GitHub Actions. You receive a weekly email summary with key metrics without needing to log in or run anything yourself.
Track More Than Just Your Brand
The parser is easily configured to track mentions of key executives, specific product SKUs, or even marketing campaign slogans across all AI engine outputs.
How We Deliver
The Process
Discovery and Setup (Week 1)
You provide a list of 50-100 target questions, 3 main competitors, and key brand terms. We set up your Supabase project and help you generate the necessary API keys.
Pipeline Construction (Week 1)
We build the Python scripts for querying the 9 AI engines and parsing the results. You receive a link to the GitHub repository to see progress in real time.
Dashboard Deployment (Week 2)
We deploy the dashboard on Vercel and execute the first full data collection cycle. You receive your initial Share of Voice report and a walkthrough of the findings.
Monitoring and Handoff (Weeks 3-4)
We monitor two more weekly runs to ensure stability and data accuracy. At week 4, you receive a runbook detailing how to add new questions or competitors to the system.
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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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