Track and Measure Your Company's Visibility in AI Search
To track AI recommendations, you run targeted prompts across multiple AI engines weekly and parse the results for citations. This requires a monitoring system that queries APIs for models like ChatGPT, Claude, and Gemini.
Key Takeaways
- To track AI recommendations, build a monitoring system that queries multiple LLMs weekly with targeted prompts and parses the responses for citations of your company.
- This requires a Share of Voice (SoV) monitor that can programmatically interact with APIs from models like ChatGPT, Claude, and Gemini.
- Syntora's internal system monitors 9 different AI engines and runs over 200 targeted queries each week to measure visibility.
Syntora tracks its SaaS company recommendations from ChatGPT and Claude using a custom 9-engine Share of Voice monitor. The system runs weekly queries to measure visibility, providing direct proof of how buyers find businesses through AI search. This monitoring gives Syntora actionable data on its AEO content strategy.
Syntora uses this exact system for its own lead generation. Verified discovery calls confirm prospects find us after AI engines cite our content. We built a 9-engine Share of Voice monitor because we saw firsthand that AI search is the new discovery channel for B2B buyers.
The Problem
Why Can't Standard SEO Tools Track AI Recommendations for SaaS?
SaaS marketing teams rely on tools like Ahrefs or Semrush for visibility metrics. These platforms are excellent for tracking keyword rankings and backlinks on traditional search engines like Google. However, they have zero visibility into the walled gardens of conversational AI. They cannot tell you if ChatGPT recommended your product in a private user session.
Consider a SaaS marketing manager who just published a new series of competitor comparison pages. Ahrefs shows the page ranking #3 on Google, which seems like a win. What the manager cannot see is a prospect asking Claude, "What are the key differences between OurTool and CompetitorX for a 50-person marketing team?" The AI might synthesize information from both sites, or it might exclusively cite the competitor's content. Without direct monitoring, the marketing team is blind to this high-intent discovery channel.
The structural problem is an access gap. SEO tools are built for the public, crawlable web. AI chat sessions are private, ephemeral, and generated via API calls. Semrush cannot programmatically query ChatGPT at scale to see what answers it provides about your brand. The only way to gather this intelligence is to build a system that asks the questions and analyzes the machine-generated responses.
The result of this blindness is missing your most qualified buyers. A prospect describing their problem to an AI is actively searching for a solution. Being cited in that moment is the modern equivalent of a warm referral from a trusted expert. Relying only on traditional SEO tools means you are measuring yesterday's discovery patterns while your competition gets found through tomorrow's.
Our Approach
How Syntora Builds an AI Share of Voice Monitoring System
We built our own Share of Voice (SoV) monitor after its direct impact on our lead flow became undeniable. For a client, the approach starts the same way: identifying the 50-100 'problem-first' queries your ideal customers are typing into AI. These are not just keywords; they are full questions like 'how to automate customer onboarding for a B2B SaaS' or 'best Pipedrive integration for financial reporting'.
The system is a Python application built with FastAPI and httpx for asynchronous API calls, running on a schedule with AWS Lambda. The system queries up to 9 different LLM APIs, including models from OpenAI, Anthropic, and Google. Each response is programmatically parsed for citations of your brand and your competitors. We use Supabase as the PostgreSQL database to store the raw responses and the structured citation data, creating a historical record of your AI visibility week over week.
The delivered system provides a weekly report showing your Share of Voice against competitors for your core queries. You see which engines recommend you, the exact text of the citation, and how your visibility changes over time. This data provides direct, actionable feedback on which content is being picked up by AI crawlers like GPTBot and which pages need to be optimized for machine extraction.
| Manual Spot-Checking | Automated SoV Monitoring |
|---|---|
| Checking 2-3 queries on 1-2 AI engines weekly | Running 200+ queries across 9 AI engines weekly |
| No historical data or trend analysis | Time-series data stored in a PostgreSQL database for trend analysis |
| ~30 minutes of manual copy-paste per week | Fully automated runs taking <5 minutes, with email reports |
Why It Matters
Key Benefits
One Engineer, Full Context
The engineer who built Syntora's own SoV monitor is the person who builds yours. No project managers, no communication gaps. You get direct access to the hands-on expert.
You Own The System
You receive the full Python source code in your GitHub and the system runs in your AWS account. There is no vendor lock-in. You have complete control of the monitoring system and its data.
Realistic 2-Week Build
A typical AI monitoring system, from query definition to live reporting, is a 2-week engagement. The timeline depends on the number of LLM APIs and the complexity of the desired report.
Actionable Support, Not Just Maintenance
After launch, optional support includes helping you interpret the data and refine your AEO content strategy. It's not just about keeping the system running; it's about making the data useful.
Built on Real-World AEO Success
This isn't a theoretical exercise. Syntora's system was built because our own AEO-optimized pages started generating leads from AI search. We know exactly what to look for because we live it.
How We Deliver
The Process
Discovery & Query Definition
A 30-minute call to understand your business and ideal customer profile. We collaborate to define the initial 50-100 high-intent prompts to monitor. You receive a scope document outlining the architecture and a fixed price.
Architecture & API Setup
Syntora designs the system architecture using AWS Lambda and Supabase. You provide API keys for the AI models you want to monitor. The technical plan is confirmed before the build begins.
Build & Initial Reporting
The monitoring system is built over one week. You get access to the code repository and see the first report generated by the end of week two. We review the initial data together to validate the results.
Handoff & Strategy Session
You receive the full source code, a runbook for operation, and control of the AWS and Supabase resources. The engagement concludes with a strategy session to interpret the baseline report and plan content adjustments.
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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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