Wellows Now Tests AI Visibility the Way Users Actually Experience It

Written by Masab Gadit

Masab Gadit

Founder and CEO

I'm Masab Gadit, Founder and CEO of Wellows, where we're building the future of AI-driven marketing automation. Over the past decade, I've dedicated myself to building and scaling digital businesses—from running one of the most efficient affiliate content networks to now leading an AI-first marketing platform that's reimagining how brands achieve visibility in the age of LLMs. My journey began with mastering Google SEO, affiliate growth, and systemized content operations. But as the digital landscape evolved, I recognized a fundamental shift: we're moving toward a world where AI-first engines like ChatGPT, Gemini, Claude, and Perplexity are reshaping how discovery happens. This realization led to Wellows—a platform where we've envisioned to create Autonomous Marketers that don't just execute tasks, but strategize, create, optimize, and continuously self-improve. These AI agents learn from user feedback, adapt to your brand's unique data, and become star performers on your growth team. At Wellows, we're not just helping brands grow; we're building the Visibility Stack that ensures brands stay cited, discoverable, and trusted across both traditional search engines and emerging AI platforms. Our vision is simple: every brand, regardless of size, should have access to its own self-learning, always-on growth engine. We're building toward a future where marketing automation isn't just about efficiency—it's about creating meaningful connections and sustainable growth.

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2 min read February 20, 2026

When you ask an LLM a question inside its consumer app, you’re not just querying a model.

This difference directly impacts how AI visibility is measured across platforms like ChatGPT, Perplexity, Gemini, and Google AI Overviews.

You’re querying an entire product system: system prompts, safety layers, UI tooling, browsing and retrieval, ranked cards, image packs, commerce modules, experiments, and sometimes personalization.

But when you call that same provider through an API, you often get something meaningfully different.

That mismatch became a serious problem for what we’re building at Wellows: accurate AI visibility measurement based on real user-facing experiences, including e-commerce queries where images, products, and “answer layouts” matter as much as text.

So we upgraded our infrastructure.

We no longer rely on LLM APIs as our primary truth. Instead, we run browser-based retrieval per query, per LLM, so the outputs we capture align with the real user-facing experience. Yes, even when the result includes images for commerce queries, or when it doesn’t.

The core issue: API answers ≠ real AI visibility experience

APIs are designed for developers. Consumer apps are designed for users. Those two goals produce different output characteristics:

Different system instructions and hidden policies
Different tools (browsing/retrieval, shopping cards, citations, UI ranking layers)
Different formatting and multimodal rendering (images/cards vs plain text)
Different A/B experiments and release trains
Different session context behavior

Our research showsThe gap is significant: only 24% brand overlap between API and UI-parity results, with just 4% source overlap. API responses average 406 words while real user-facing responses average 743 words.

The result is simple: API outputs are not a faithful proxy for what users see day-to-day.

And if your product depends on user-perceived results, like brand presence, product inclusion, or share of voice inside AI answers, API-only testing can quietly mislead you.

“If you’re measuring AI visibility using APIs, you might be measuring a developer interface, not the user reality.“

What Changed at Wellows?

We rebuilt our pipeline to emphasize UI-parity capture:

Before

We used API to extract AI responses on ChatGPT, Perplexity, Google AI Overviews, Gemini and AI Mode.

Now

Now, we no longer use API. We get live answers from AI platforms:

Call model/provider APIs normalize outputs
Score brand presence, citations, and ranking signals

visibility-score-and-citations-insights-on-wellows

Run each query through browser-based user session simulation

wellows-generating-response-in-real-time

Capture what the user would actually see (text + layout signals, and image availability where applicable)

Extract and normalize outputs for measurement

In practice: each query runs “against each LLM” as a real user session would, rather than via a simplified API response.

Why Experience-level Testing Produces More Realistic Results

Experience-level testing produces more realistic results because:

1) You get the same rendering path users see

Consumer experiences often include:

Rich answer blocks
Inline citations
Product cards
Image panels
“Top picks” modules
Layout-driven prominence

These are frequently absent or reduced in API responses.

2) You capture the “real distribution” of answers

Consumer LLM products can behave like living systems: experiments, UI changes, and ranking tweaks roll out constantly. Our front-end parity pipeline reflects that live reality.

3) E-commerce queries are inherently multimodal

For commerce, “did the model mention my brand?” is only half the question. The other half is:

Did it show products?
Did it show images?
Did it cluster competitors visually?
Did it choose a marketplace and not another?

Our research foundAPIs miss brand detection 8% of the time, while UI-parity capture consistently detects brand presence. Browser-level measurement is the only reliable way to assess that experience end-to-end.