Oct 8, 2025
For months now, I've been telling clients the same thing: if you aren't tracking LLM responses, you can't possibly be optimizing for Answer Engine Optimization (AEO).
That statement tends to get one of two reactions. Either a knowing nod from someone who's already neck-deep in the problem, or a blank stare from someone who's about to learn just how complex the new world of AI-driven discovery has become.
Here's the reality: your potential customers are no longer just Googling you. They're asking ChatGPT, Claude, Perplexity, and Gemini for recommendations. And you have no idea what these AI systems are saying about you—or worse, which of your competitors they're recommending instead.
This isn't theoretical. This is happening right now, at scale, and most B2B software companies are completely blind to it.
The Manual Tracking Nightmare
Like many of you, I started tracking this the hard way. I'd run the same prompt across all the major LLM platforms manually. Copy the prompt into ChatGPT. Then Claude. Then Perplexity. Then Gemini. Screenshot or copy-paste the results into a spreadsheet.
It worked, sort of. I could see which brands appeared. I could spot patterns.
But then the questions started piling up: Which model version gave that answer? Was it GPT-4 or GPT-4o? Which competitors appeared most frequently across all models? Why is Competitor X showing up in Claude but not ChatGPT? Which citations are they using?
What started as a simple tracking exercise quickly turned into a full-time data management project. The spreadsheets became unwieldy. The insights got buried. And I knew there had to be a better way.
The "I'll Just Build It Myself" Phase
Like any technologist facing a problem, my first instinct was: "How hard can it be to build my own tracker?"
I fired up Cursor and RooCode, armed with the confidence that agentic AI coding would solve this in a weekend. I had visions of a custom-built LLM tracking dashboard, perfectly tailored to my needs, running beautifully within days.
Reality hit hard.
I got something working—technically. It ran prompts. It captured responses. But every new feature request introduced three new bugs. The AI would confidently add functionality that broke existing code. I'd spend an hour implementing a feature, then three hours debugging what it broke elsewhere.
One step forward, three steps back.
The time investment quickly became untenable. I'm a marketing strategist and lead generation expert, not a full-stack developer with unlimited time to babysit an increasingly fragile codebase. The juice wasn't worth the squeeze.
And then, as if the universe heard my frustration, purpose-built LLM tracking solutions started hitting the market.
The Commercial Tools: What Actually Exists Today
The LLM tracking space is exploding. New tools are launching almost weekly. Some are basic. Some are sophisticated. Some are wildly overpriced. Here's what I've tested personally:
BrandSignal.ai – The DIY Starter Kit
This was my first foray into LLM tracking, and honestly, it felt a lot like the simple tracker I'd built myself in Cursor. It's very basic and still requires a fair amount of manual work. If you're just starting to wrap your head around LLM tracking and need something simple, it's a low-cost entry point. But you'll outgrow it fast.
Rankshift.ai – The Power User's Choice
This is where things get more serious. Rankshift offers better import/export functionality, which is critical when you're running bulk prompt tests across dozens of variations. The ability to export full citation lists is a game-changer for understanding which sources the LLMs are pulling from.
The standout feature for my world: you can accommodate multiple products within a single group. This is essential for PE rollups managing multiple acquired brands under one portfolio. You're not juggling separate accounts for each entity—everything lives under one roof.
Promptwatch.com – Pretty but Problematic
I wanted to love Promptwatch. The visuals are stunning—easily the best-looking interface of the bunch. But style over substance is a real problem here. Despite the sleek UI, it's actually the least usable tool I tested. The workflows felt clunky, and I found myself fighting the interface more often than gaining insights from it. Sometimes beautiful design gets in the way of getting work done.
GPTrends.io – The Balanced Contender
If I had to recommend a single tool to most businesses today, it would probably be GPTrends.io. It strikes the best balance between functionality and form. The pricing is reasonable, the features are robust, and it doesn't overwhelm you with unnecessary complexity.
Two features stand out: the matrix view, which lets you see how different models respond to the same prompts side-by-side, and the GPT-generated tactical recommendations. The AI doesn't just show you where you're ranking—it suggests specific, actionable steps to improve your visibility. That alone justifies the subscription cost.
TryProfound.com – Enterprise-Grade (and Enterprise-Priced)
For larger organizations or enterprise-level clients, Profound might make sense. It's comprehensive, makes ambitious claims about its capabilities, and positions itself as the premium solution in the space.
The catch: it's very pricey. Unless you're managing LLM optimization across a massive portfolio with significant budget, the ROI calculation gets tough. Most mid-market B2B software companies will find better value elsewhere.
Ahrefs and Semrush – The "We Have LLM Tracking at Home" Option
Both of the traditional SEO heavyweights have added LLM tracking features. Your mileage will vary.
I haven't tested Semrush's offering yet, so I can't speak to it directly. Ahrefs' implementation has one compelling feature: it will show you where your brand or website is appearing across their entire LLM result index. This means you can discover new prompts you didn't even know you were ranking for—valuable competitive intelligence you won't get from testing only your own prompt lists.
The major limitation: you can't check custom prompts. You're stuck with whatever Ahrefs decides to track. For discovery, it's useful. For strategic optimization around specific buyer journeys or use cases, it's far too limited.
The Brutal Truth About Prompt Volume Data
Here's something every vendor conveniently glosses over: there is no reliable way to determine actual LLM prompt volume demand. None. Zero. If a tool claims to show you "search volume" for ChatGPT prompts, they're extrapolating from Google search data and making educated guesses about how that might translate to LLM usage.
It's not necessarily dishonest—it's just the reality of an emerging channel where the platforms don't publish query data. Take any "volume" metrics with a massive grain of salt. They might directionally indicate popularity, but they're not hard numbers you can bank on.
The Referral Traffic Trap: What Your Analytics Aren't Telling You
Before we talk about which tracking tool to choose, we need to address the elephant in the room: LLM referral traffic in your analytics.
If you're checking Google Analytics 4 for traffic from ChatGPT, Perplexity, or Claude, you're looking at real data—but you're only seeing a fraction of the story. A small fraction.
Here's why: referral traffic only occurs when two conditions are met. First, the LLM must present your brand with a clickable link in its response. Second, the user must actually click that link. Both conditions are required. No link, no click, no data.
The problem? Most citations in LLM responses are naked brand mentions. The AI says "Company X offers this solution" without providing a hyperlink. Or it mentions you in a list of competitors but only links to two of them. Or it describes your product accurately but directs the user to your competitor's site instead.
In all these scenarios, you're getting brand recognition and awareness—the user now knows you exist and what you do—but you're getting zero referral traffic to measure. Your analytics show nothing. You appear invisible.
This creates a dangerous blind spot. If you're only tracking referral traffic, you're dramatically underreporting the actual volume of brand mentions and exposure your company is receiving in LLM responses. You might be showing up in hundreds of AI-generated answers every week, shaping buyer perceptions and consideration sets, while your analytics suggest LLMs are barely a factor.
Does this mean you shouldn't track LLM referral traffic? Absolutely not. You should absolutely be tracking it, and you should be setting it up properly. Search Engine Land has a solid guide on how to segment LLM traffic in GA4 if you haven't already configured this.
Referral traffic from LLMs is a good indicator—it tells you when you're winning the full conversion journey from AI mention to website visitor. It shows you which prompts or topics are driving engaged users who care enough to click through. That's valuable data.
But it's not the full picture. Not even close.
This is precisely why dedicated LLM tracking tools exist. They show you every mention, every citation, every time your brand appears in an AI response—regardless of whether it came with a clickable link or whether anyone clicked. They reveal the complete landscape of your AI visibility, not just the tiny sliver that makes it into your analytics.
Track your referral traffic. Analyze it. Optimize for it. But never mistake it for the complete story of your LLM presence.
So What Should You Actually Do?
This field is changing at a breakneck pace. What's true today might be obsolete in three months. I don't have a single definitive recommendation because your needs will vary based on your business size, your portfolio complexity, and your budget.
Here's my advice: try a few tools. Most offer free trials or low-cost starter plans. Test them with real prompts relevant to your business. See which interface you actually want to use daily, because the best tool is the one you'll actually use consistently.
But here's the non-negotiable part: you must be tracking something. Anything. Even if it's just a basic tool or a well-organized spreadsheet.
Because the fundamental principle remains unchanged: if you aren't tracking LLM responses, you have no idea what these AI systems are telling potential customers about you. You can't optimize what you can't measure. You can't improve your visibility if you don't know where you currently stand.
The B2B software companies winning in this new AI-driven world aren't the ones with the best websites or the highest SEO rankings. They're the ones who recognized early that the game changed, invested in understanding how LLMs represent them, and systematically optimized their presence across these new discovery channels.
Start tracking. Start testing. Start optimizing.
The future of B2B lead generation isn't coming—it's already here. And it speaks in tokens, not keywords.
Managing Partner, Ignite Marketing
Review by:
Subject Matter Expert
Marketing Technologist and B2B Lead Generation Expert
