Profound vs Serge

Profound measures whether ChatGPT mentions you. Serge measures what happens when the agent actually arrives.

Both tools live in the world of AI-driven commerce. They do not compete for the same question. Profound sits upstream of the visit — is your brand cited in AI answers? Serge sits downstream — when ChatGPT or Claude sends a customer to your site and the agent tries to buy on their behalf, does the purchase complete?

If you're already running Profound, AthenaHQ, or Scrunch, you have the first half of the picture. This page explains what the second half looks like and why most teams need both once they start taking AI-mediated commerce seriously.

The three layers

Where each tool sits in the AI-commerce journey

AI-mediated shopping has three distinct measurement problems. Confusing them is the single most common mistake teams make when they first try to instrument the channel.

Layer	The question it answers	Who covers it	What you learn
01 Visibility	Does ChatGPT mention us when a user asks?	Profound, AthenaHQ, Scrunch, Peec, Otterly, Semrush AI, Adobe LLM Optimizer	Share of voice in AI answers
02 Arrival + traversal	Can the agent find our products and buy them?	Serge. Category is otherwise empty.	Where the agent fails and what to fix
03 Attribution	What share of revenue came via AI-mediated traffic?	Dreamdata, HockeyStack, Bizible (partial — broken for agent sessions)	Revenue impact per channel

The three layers are sequential. Visibility without arrival measurement means you know the agent heard about you but not whether anyone actually got through. Arrival without visibility means you can fix conversion but don't know whether agents are recommending you in the first place. Most teams start at one layer and discover they're missing the other within the first quarter.

Side by side

What each tool actually measures

Profound

AI answer visibility

Profound queries large language models with the prompts your customers might use, and tracks whether and how your brand appears in the answer. It's the right tool for the upstream question: are we in the conversation at all?

→Runs prompts against ChatGPT, Claude, Perplexity, Gemini on a schedule
→Measures share of voice, citation frequency, and sentiment
→Tracks position and brand mentions across AI surfaces
→Surfaces which prompts do and don't name you

Serge

Agent conversion on your site

Serge measures the next leg of the journey. Once an AI agent actually lands on your site — because a user asked ChatGPT or Claude to buy something — can it find the product, operate the variant selector, and reach the cart? A deterministic scan grades your store; an active replay captures a real agent trying the task.

→Scans your site for structural barriers that stop agents completing purchases
→Detects agent-driven sessions in production with a lightweight JS snippet
→Replays a real Claude or ChatGPT agent against your PDP and checkout
→Produces a fix list — which DOM, schema, and interaction issues to ship

The honest answer

You probably need both, not one

A Product Director who runs only Profound sees that 34% of ChatGPT answers in their category cite their brand — and has no idea whether any of those referrals turn into sales. A Product Director who runs only Serge sees that agent traffic fails at the variant selector 47% of the time — and has no idea whether AI is recommending them in the first place.

The two halves compound. Profound tells you the top of the funnel is working. Serge tells you the bottom of the funnel is working. If either half is broken, the whole channel is broken, and you can't tell which from one tool alone.

This is why most of the enterprise e-commerce teams we talk to run both — usually Profound first (visibility is easier to pitch internally) then Serge once they realize they've been sending agent traffic into a site it can't operate.

Common questions

What we get asked most

Is Serge a replacement for Profound?

No. Profound answers a question Serge doesn't try to answer (are we in the AI conversation?), and Serge answers a question Profound doesn't try to answer (can the agent complete a purchase on our site?). They measure different layers of the same problem.

Why not just query ChatGPT ourselves to test the full journey?

You can, and we do — that's Serge's active replay. But a single replay run is expensive and non-reproducible at scale. A daily scan is cheap and deterministic, so you measure structural readiness across every page and use replay for the deep dive when the scanner flags something worth investigating.

How do you measure agent failure without running a real agent every time?

The scanner is deterministic: it inspects the structural properties that correlate with agent traversal success — schema.org coverage, DOM semantics, variant selector ARIA roles, bot-protection posture, crawlability for GPTBot and ClaudeBot, and a handful of other signals. When you want ground truth on a specific page, the active replay runs a real Claude or ChatGPT agent end-to-end. The methodology is published.

If we already have Hotjar or Contentsquare, isn't this covered?

No. Hotjar and Contentsquare are human-session tools — they record clicks, scrolls, and form interactions from humans. Agent sessions don't generate those signals (no mouse movements, no hover events) so they drop out of CRO replay entirely. Serge is built for the sessions the CRO tools can't see.

See also: Serge's published scoring methodology, which explains exactly what the scanner checks and how the score is calculated.

Scan gratuit · 30 secondes · sans inscription

Vous voulez un instantané de votre situation ?Scannez votre boutique.

Collez votre domaine. Serge parcourt votre boutique en environ trente secondes — entièrement déterministe, sans IA dans la boucle de scan — et renvoie un instantané rapide : où les agents peuvent être bloqués, quoi inspecter en premier, et les corrections suggérées que votre équipe peut vérifier. Sans inscription. Transmettez le résultat à votre lead front-end.

Le scan est le point d'entrée. Lancez un vrai agent IA contre votre store sur Pro et regardez où il échoue — snippet de correctif par échec.