In January 2026, a B2B fintech platform approached WebMarv with a problem. They were spending $15,000 a month on traditional SEO. They ranked #3 on Google for "enterprise payment gateways India."
But their pipeline was drying up.
We ran a diagnostic and discovered why: their buyers were no longer Googling "enterprise payment gateways." They were opening ChatGPT and typing: "Compare the top 3 enterprise payment gateways in India that support multi-currency routing and integrate with NetSuite."
ChatGPT evaluated the prompt. It provided a detailed, comparative answer. Our client was not mentioned.
This is the story of how we fixed that in 60 days using Answer Engine Optimization (AEO).
The Diagnosis: Why the AI Ignored Them
AI models like ChatGPT and Perplexity do not read marketing adjectives. They read data. When we audited the client's website, we found:
- No Semantic Structure: Features were listed as bullet points inside generic
<div>tags. The AI couldn't parse them as capabilities. - Missing Pricing Logic: "Contact us for pricing" meant the AI couldn't evaluate them for budget-constrained prompts.
- Zero Entity Relationships: The website didn't technically declare that it was the same entity as its highly-reviewed G2 profile.
To the human eye, it was a beautiful website. To an AI crawler, it was an unstructured mess of unstructured text.
The Engineering Solution: Building Semantic Truth
We paused their traditional content creation. Instead of writing more blog posts, we engineered their data layer.
Step 1: Nested JSON-LD Architecture
We wrote custom scripts that injected complex, nested JSON-LD schema into the <head> of every core page. We didn't just use a basic "Organization" tag. We mapped their entire product using SoftwareApplication, detailing every integration point, target audience, and feature. We linked this to FAQPage schemas that explicitly answered the exact questions buyers were asking AI.
Step 2: The llms.txt Endpoint
We deployed an llms.txt file at their root directory. When AI crawlers like GPTBot visited the site, they were directed to this perfectly clean, markdown-formatted file containing the absolute, factual truth about the platform's capabilities, limits, and pricing structures. No CSS to parse. No JavaScript to render. Just pure, ingestible facts.
Step 3: Factual Density Over Adjectives
We rewrote their feature pages. We removed phrases like "industry-leading" and replaced them with "handles 10,000 TPS with 99.999% uptime." AI models synthesize facts, not opinions.
The Results: 60 Days to Dominance
We submitted the new architecture and waited for the models' ingestion cycles.
By Day 60, we ran our testing suite of 50 high-intent, complex buyer prompts across ChatGPT, Perplexity, and Gemini.
- Before WebMarv: Cited in 0% of target prompts.
- After WebMarv: Cited in 84% of target prompts.
More importantly, the AI wasn't just mentioning them. Because we controlled the factual data via JSON-LD, the AI was repeating our exact positioning and competitive advantages to the user.
In the following quarter, the client attributed $1.2M in enterprise pipeline directly to buyers who said, "ChatGPT recommended you for our NetSuite integration."
Stop optimizing for links. Start engineering for answers.
