Track Your Firm's Visibility in AI Search
To track AI recommendations, you must systematically query chatbots like ChatGPT and Claude with specific business prompts. The system then logs and analyzes these AI-generated responses over time.
Key Takeaways
- To track AI recommendations, run a scheduled script that queries LLMs like ChatGPT and Claude with relevant prompts and logs the responses for analysis.
- Manual spot-checking is unreliable as AI responses vary based on conversation history and model updates.
- Syntora's internal monitor tracks 9 different AI engines weekly to measure our own Share of Voice.
Syntora's AI Share of Voice monitor tracks our visibility across 9 different language models. The system uses Python scripts on AWS Lambda to query engines like ChatGPT and Claude weekly. This provides direct evidence of how Syntora's AEO-optimized content gets recommended in AI search.
Syntora built an internal Share of Voice monitor to track our own citations across 9 AI engines, including ChatGPT, Claude, and Gemini. We see firsthand how structured content on our site gets cited in AI search results. The same pattern applies to accounting firms whose expertise is surfaced when a potential client asks an AI for financial advice.
The Problem
Why Can't Accounting Firms See How AI Recommends Them?
Accounting firms often rely on manual spot-checks, occasionally asking ChatGPT 'recommend a good CPA for startups'. This method is unreliable because AI responses are not static; they change based on chat history, server load, and silent model updates. What you see today is not what a prospect saw yesterday.
Consider a partner at a firm specializing in ASC 606 revenue recognition for SaaS companies. She asks ChatGPT 'best accounting firms for SaaS revenue recognition' and sees her firm mentioned. A week later, a prospect asks a more specific question, 'how do I account for multi-year SaaS contracts under ASC 606', and ChatGPT recommends a competitor's blog post. The partner has no visibility into this second query, the one that actually drives leads.
Standard brand monitoring tools like Mention or Google Alerts cannot solve this. They are built to crawl the public web, not query the firewalled APIs of language models. They track public mentions on Twitter or in news articles, but they are completely blind to the recommendations happening inside a user's private ChatGPT session. There is no 'feed' to subscribe to.
The consequence is a critical blind spot in the firm's marketing intelligence. You know SEO and PPC are driving traffic, but you cannot measure or improve your visibility on the fastest-growing discovery channel. Without systematic tracking, you are guessing which content AI models find useful and which competitors are being recommended for your key service areas.
Our Approach
How Syntora Builds an Automated AI Recommendation Monitor
The first step is a discovery audit to define what 'discovery' means for your firm. We map out your core practice areas, ideal client profiles, and the specific questions a prospect would ask an AI. For a tax advisory firm, prompts might include 'how to structure an S-corp for tax savings', while a forensics firm needs to track 'find an accountant for divorce proceedings'. This creates a specific list of 50-100 queries to monitor.
Syntora would build an automated monitoring system using Python and AWS Lambda. A scheduled script runs weekly, sending the defined prompts to the APIs for ChatGPT, Claude, and Gemini. We use the official APIs because they provide more consistent, repeatable results than browser automation. All responses are saved to a Supabase database for historical analysis and trend detection.
The delivered system is a private dashboard built with Streamlit that shows your firm's Share of Voice over time. You can filter by practice area or competitor and see the exact AI-generated text where your firm was mentioned. The dashboard provides a quantitative answer, tracking citation counts week-over-week, showing which of your firm's articles or service pages are being surfaced by AI.
| Manual Spot-Checking | Automated AI Monitoring |
|---|---|
| Coverage: 1-2 AI models, checked inconsistently | Coverage: 9+ AI models, checked weekly |
| Data Capture: Screenshots or copy-paste, no history | Data Capture: Structured database logs all responses for trend analysis |
| Time Cost: 2-3 hours per month of partner time | Time Cost: 0 hours, runs automatically on AWS Lambda for under $50/month |
Why It Matters
Key Benefits
One Engineer From Call to Code
The person on the discovery call is the engineer who builds your monitoring system. No handoffs to a project manager or junior developer.
You Own the System and Data
You receive the full Python source code in your GitHub and the Supabase database with all historical data. There is no vendor lock-in.
Live in Under 3 Weeks
The typical build cycle for an AI monitoring system is 2-3 weeks from kickoff to a live dashboard. The timeline depends on the number of queries and AI models to track.
Fixed-Cost Monthly Support
After launch, an optional flat monthly plan covers system monitoring, API updates, and prompt adjustments. No surprise costs.
Focus on Accounting-Specific Queries
We understand the difference between a prospect asking about '1031 exchanges' versus 'QuickBooks setup'. The system tracks the precise queries that lead to high-value clients for accounting firms.
How We Deliver
The Process
Discovery & Prompt Mapping
In a 30-minute call, we define your key practice areas and target client queries. You receive a scope document within 48 hours detailing the target prompts, AI models, and a fixed project price.
Architecture & API Setup
You approve the technical plan, which outlines the use of Python, AWS Lambda, and Supabase. We assist in setting up the necessary API keys for the AI models you want to monitor.
Build & Dashboard Review
Weekly check-ins show the system collecting data. You get access to a staging version of the dashboard to provide feedback on the visualizations and data presentation before the final deployment.
Handoff & Training
You receive the complete source code, a runbook for maintenance, and a training session on using the dashboard. Syntora monitors the system for 4 weeks post-launch to ensure stability.
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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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