How Kora Increased Primary AI Citations by 35% in 90 Days - Without Changing a Single Marketing Message

A fintech infrastructure company was invisible to AI models - not because its product was weak, but because its documentation was written for humans instead of machines.

Company Overview

Kora provides cross-border payment infrastructure for businesses scaling across Africa. Their product suite includes card issuance, Banking as a Service (BaaS), and API-driven payment rails - technical capabilities that sit beneath consumer-facing products rather than in front of end users. Kora's customers are developers, fintech founders, and enterprise finance teams solving complex regional payment problems.

The Challenge

When users asked ChatGPT or Perplexity which payment infrastructure was best for scaling in Africa, Kora was routinely excluded. The models instead surfaced legacy brands with higher overall search volume but less relevant technical capabilities.

The problem wasn't Kora's product - it was how Kora was classified. Their existing documentation was written for developers who had already found their site: dense, long-form, expert-level guides that assumed prior context. For an AI crawler trying to build a knowledge map of the payment ecosystem, this documentation was largely a black box.

Without a clear semantic structure, models defaulted to categorizing Kora loosely as an "Africa fintech" player rather than a "Cross-Border Infrastructure Leader" - a categorization too vague to generate confident citations for specific infrastructure queries.

There was also a hallucination problem: models were generating incorrect claims about unsupported regions, compounding Kora's visibility challenge with a credibility one.

A prospect told us they heard we didn't support Kenya. We absolutely do. That's when we realized AI wasn't just ignoring us, it was actively giving people wrong information about our business.

- Olawale Akinola, Marketing Lead at Kora

Why They Chose This Approach

The core diagnosis was a knowledge gap - not a content gap. Kora didn't need more pages or more marketing copy. They needed to restructure what already existed into a format that AI models could parse, trust, and cite. The goal was to lower the "compute cost" for any model trying to understand Kora's specific value proposition.

The Strategy

Step 1: Entity Relationship Mapping

Using the Pierview Entity Scanner, the team audited how models currently link Kora to related concepts. The scan revealed a strong association with "Africa" (as a location) and a weak association with "infrastructure" or "capability" - the exact inverse of how Kora wanted to be positioned.

The fix: implement the Organization and Service schema that explicitly maps Kora's relationship to Banking as a Service and Card Issuance. The goal was to shift Kora's entity classification from a geographic player to a capability-defined infrastructure provider.

Step 2: The 200-Word Atomic Rule

Kora's existing technical guides averaged 3,000 words - appropriate for developers deep in implementation, but opaque to an AI summary agent looking for a quotable answer.

Every high-value technical section was broken into Atomic Answer Blocks. Each block followed the same structure: state the process, name the compliance standard, define the outcome - all in under 200 words. An AI agent hitting any of these sections could extract a complete, accurate citation without needing to parse the surrounding 2,800 words.

Example H2 target: "How does Kora handle cross-border settlement?" - rewritten from a multi-section walkthrough into a single standalone paragraph covering mechanism, compliance, and result.

Step 3: Deploy llms.txt

Kora was among the earliest in their niche to deploy a standardized llms.txt file - a structured directive that tells AI crawlers exactly where to find specific types of content. Rather than letting bots guess which content to prioritize, the file explicitly pointed crawlers to API specifications (for technical queries) versus marketing copy (for general awareness queries).

This separation ensured that when a model asked a technical infrastructure question, it retrieved technical data - not sales language - resulting in more accurate, more credible citations.

The Results

Tracked across 10 complex payment infrastructure prompts on GPT-5, Gemini, and Perplexity over 90 days:

• 35% increase in primary citations (first or second recommendation for regional payment infrastructure queries)
• ~0 incorrect "unsupported regions" claims - down from a persistent baseline, as models shifted to citing structured llms.txt data
• +18% conversion rate from AI-referred traffic - the AI was pre-qualifying visitors against Kora's actual capabilities before they arrived

Before this, we were only showing up in about 2 out of 10 relevant searches. And when AI did mention us, it got our regional coverage wrong nearly half the time. The +18% conversion bump proved we weren't just getting more traffic, we were getting better traffic.

- Olawale Akinola, Marketing Lead at Kora

Broader Impact

The hallucination reduction may be the most strategically significant result. For a B2B infrastructure company operating in a region where trust and accuracy matter enormously to enterprise buyers, having AI models routinely generate false claims about service coverage is a pipeline problem, not just an SEO problem. Eliminating those incorrect citations protects deals in progress, not just top-of-funnel visibility.

Looking Ahead

Kora's documentation restructuring is now a permanent part of their technical publishing workflow. Every new API release and regional expansion is formatted to machine-readable standards before it goes live - making future AI citation gains cumulative rather than one-time.

Now it's just part of our workflow. Every new API, every new market launch gets the same treatment before we publish. The AI's understanding of what we do gets better with each release, not worse.

- Olawale Akinola, Marketing Lead at Kora