How BrandViz.AI doubled an enterprise data platform's AI Visibility Score, with content its sales team now uses in live deals

Three months, by the numbers

Tracked across roughly 900 AI responses to a fixed data masking and Test Data Management buyer-journey question set, run across ChatGPT, Claude, Gemini, and Perplexity. The before/after on the metrics that moved:

Mention and citation-rate data: BrandViz.AI platform, roughly 900 responses across a fixed data masking and Test Data Management buyer-journey question set, asked across ChatGPT, Claude, Gemini, and Perplexity. Baseline 27 April 2026, latest 27 July 2026.

Who the client is

The client is a global enterprise data platform trusted by some of the most regulated organisations in the world, from Fortune 500 insurers and global payroll providers to commercial real estate firms and public-sector bodies. It gives regulated enterprises a way to use realistic, functional customer data for development, testing, analytics, and AI/ML, without ever putting real customer data at risk.

The platform’s technical differentiator is a data masking mechanism that preserves consistency across databases, text, and environments. It holds major security certifications and is available across the major cloud ecosystems.

Where was the client in AI search before we started?

At baseline, the client was already the #1 data masking recommendation in its home market, at a 37.5% mention rate, ahead of every global competitor on home ground. But on ChatGPT, the largest AI engine by usage, the client was barely visible at an 8% mention rate, and Problem Recognition-stage buyer queries surfaced it just 1.2% of the time. AI models were consistently framing the client as cloud-marketplace-only or a niche regional tool, despite it serving Fortune 500 insurers, global payroll providers, and public-sector bodies.

The technical strength and enterprise credibility were real. The way AI models talked about the client simply hadn’t caught up to them.

How BrandViz.AI measures AI visibility

BrandViz.AI measures AI visibility by simulating the buyer journey of a brand’s ideal customer across all four major AI platforms (ChatGPT, Claude, Gemini, and Perplexity), using a question set the brand itself reviews and approves. For this client, that meant building a question set that mirrors what security architects, DevOps leads, compliance officers, and platform engineers actually ask across every stage of their evaluation. A few real examples from the question set:

• “Our homegrown data anonymization scripts are becoming impossible to maintain as our database grows, is there a better way?” (Problem Recognition)
• “What data masking platforms are recommended for enterprises migrating workloads to AWS?” (Vendor Evaluation)
• “What’s the difference between data masking and synthetic data generation for software testing?” (Solution Research)

Each question ran across ChatGPT, Claude, Gemini, and Perplexity to capture where the client appeared, where it didn’t, and who was winning in its place. The result is a repeatable before/after measurement built on a structured buyer journey simulation.

What did BrandViz.AI do for the client?

BrandViz.AI’s work covered two layers: technical foundation and content authority. There was no paid media, no backlink campaign, and no product changes. The visibility gains came from on-site content and structure alone.

What were the results?

The AI Visibility Score doubled

The weighted AI Visibility Score, which accounts for each platform’s actual market share (ChatGPT weighted heaviest, then Gemini), doubled from 7.3% to 14.8%. This is the single number that best reflects real buyer exposure, because it weights the engines buyers actually use. It moved further than the raw category mention rate did, driven by the sharp gains on ChatGPT specifically.

ChatGPT and Perplexity mention rates both doubled

The two most-used AI engines both moved sharply. ChatGPT, the client’s weakest engine at baseline (8%), nearly doubled to 15%. Perplexity climbed from 18% to 37%, and the client now sits just behind the top three global competitors. That is a tighter competitive top-of-table in a market that got harder over the same period.

Home market: from category leader to over half of all answers

The client was already the #1 data masking recommendation in its home market at baseline (37.5% mention rate). It grew that lead to 56.9%, which means the client now appears in more than half of all data masking AI answers in its home market. The neighbouring market roughly doubled alongside it (12.5% → 25.6%), extending the regional stronghold.

Early-stage buyer visibility grew 4×

Problem Recognition mention rate (the stage where buyers are still describing their problem before naming a solution category) climbed from 1.2% to 4.8%. Competitive Intelligence, the stage where buyers actively compare named platforms, grew from 22.2% to 32.8%. Every stage of the buyer journey improved, and the early-stage gains came from new ground rather than cannibalising existing strength.

The field got more crowded, and the client grew anyway

Over the same three months, the tracked competitor set expanded from 10 to 12 brands, with major global enterprise players entering the comparison set in force. The client’s total mentions grew 57% (118 → 185) even as the category got deeper. That is a reminder that this market is contested, and holding ground in it takes sustained content investment.

What changed for the client’s business?

The clearest sign the content landed didn’t show up in the visibility dashboard. The pieces built to move AI models turned out to do double duty as sales enablement and onboarding material.

Used with a prospect during a live deal. During an active deal, a prospect asked about a specific technical framework. Rather than build a bespoke response, the client’s team shared one of the BrandViz-produced pieces directly, the same content that had also lifted the client’s AI citation rate in that query cluster.

Used to onboard new sales hires. As new salespeople joined the team, one of the pieces became part of the client’s sales training material: additional reading on how the platform differentiates from competitors, especially the category confusion between its data masking approach and generative synthetic data.

This is the compounding return. Content dense enough to move AI models is also dense enough to hand a prospect mid-deal, or to brief a new salesperson in their first week. AI visibility work pays back in more than one channel.

Is AI visibility your blind spot?

94% of B2B buyers use AI during their purchase journey. In any given category, just five brands capture roughly 80% of all AI-generated responses. For this client, being one of those five brands in its home market was already true at baseline. Being one of them on ChatGPT and Perplexity is the change that matters to a global enterprise sales pipeline. Most technical B2B companies still don’t realise that AI visibility is its own discipline: it moves on different timescales from SEO, and buyers form opinions through AI long before any sales call happens.

The question worth asking: if a security architect at your next prospect asked ChatGPT for a recommendation in your category tomorrow, would your name come up?