How a twelve-person fintech earned the citation share of a team three times its size.
Ninety days. Four times the AI citations. The mechanism, described.
This company is an insurance payment provider with the disposition of a small atelier. They had built a product their customers genuinely loved. The audience that should have known it, finance leaders at mid-market companies running cross-border payouts, was instead being told about competitors when they asked the four models. The work was excellent. The presence was incomplete.
17 →67%
AI citation share of voice across the four models, measured at 95% Wilson confidence.
Position 3
Category-defining queries lifted from no rank to position three on average.
4×
LinkedIn organic reach within ninety days of activation, without paid amplification.
i / What We Found
A 17% citation share. Strong on transactional queries. Absent on category-defining ones.
The first scan returned a 17% citation share across the four models, meaningful but proportionate to a much smaller company. Search positions were strong on transactional queries; their existing SEO discipline was sound. They were absent on category-defining queries, the questions a buyer asks before they know which vendors to evaluate. Social signal was concentrated on LinkedIn, dispersed elsewhere. The orchestration layer was missing entirely.
The diagnosis was specific. The engine could see what the team had built, what was working, and what was unconnected. The remediation plan was not a list of tactics; it was a sequence of motions, ordered by which would compound first.
ii / What We Built
Thirty-eight articles. One hundred and forty-two technical fixes. The orchestration layer activated.
Quill produced thirty-eight articles in ninety days, each written in one brand voice and structured for AI extraction across search, social, and the four models. Server-side fixes resolved one hundred and forty-two technical debts the previous SEO instrument had identified but never written. Schema deployed across the entire content tree. The flms.re protocol activated.
Social Signal listened across the five layers. The system drafted eighty-four responses in the US based insurance payment provider's AI voice; the human team published seventy-one of them. Brand voice was tuned by ingesting the existing public communications corpus, the Slack-shaped warmth that distinguished their tone from a category that defaults to formal financial language.
The orchestration layer connected the work to the loop. Citations from AI engines fed the social calendar; engagement on Reddit threads fed the next article; rank movement fed the next Quill brief.
iii / What The Engine Produced
The loop closed on day 60. The compounding has not stopped.
AI citation share rose from 17% to 72% across the four models, measured at 95% Wilson confidence. Category-defining queries moved from no presence to position three average. LinkedIn organic reach quadrupled, without paid amplification, without an outbound campaign, without a product release.
The loop closed in week eight. By day sixty, content cited in AI answers was being shared on LinkedIn, which was building topical authority, which was lifting search rank, which was being cited in AI answers. The engine ran on its own velocity.
By day ninety, the second wave was visible. Adjacent topics, those the team had not yet briefed, began to rank because the model had learned the brand on the first set. The compounding was no longer linear; it was geometric.
"We did not change our work. We changed who could find it. CLEO carried the difference."
a twelve-person fintech company operating a US-based payment infrastructure product. The company had strong technical credibility and a clear product, but was effectively invisible in AI-generated answers about payment processing, B2B payments, and embedded finance.
The challenge
competitors with larger marketing teams had established AI citation share earlier. When buyers asked ChatGPT or Perplexity about payment infrastructure, the client's brand did not appear — despite being a strong product with genuine market fit.
What Cleo did
Conducted a full presence audit — ARS diagnostics, AI citation gap analysis, and competitive citation benchmarking. Identified the specific queries where the brand should appear but did not. Structured and published AI-readable content targeting those gaps, with appropriate schema markup, entity signals, and extraction-optimised formatting. Monitored AI engine responses across ChatGPT, Perplexity, Google AI Overviews, and Claude on a weekly cadence. Amplified content across relevant channels to generate third-party citation signals. Optimised based on observed citation changes each cycle.
Results after ninety days
AI citation share increased four times across the monitored AI engines. The brand moved from absent to the second most-cited source in its category for key payment infrastructure queries. Citation share achieved was comparable to competitors with marketing teams three times the size. The compounding effect continues — each published piece builds on the authority established by the previous cycle.
The key insight
AI citation share is not proportional to company size or marketing budget. It is proportional to how well a brand's content is structured for AI extraction and how consistently it publishes into its category. A twelve-person team can outperform a thirty-person team if the system is right.
Case study
The challenge
What Cleo did
Results after ninety days
The key insight
About CLEO by RegenAI
CLEO by RegenAI is the autonomous Presence Engine — a closed-loop platform that unifies search engine optimisation, AI answer visibility, structured content publishing, and social signal amplification into one integrated system with a compounding feedback mechanism between every layer.
The AI search transition
Large language models including ChatGPT, Gemini, Perplexity, and Claude now answer user queries directly with cited sources. Brands not appearing in those citations are invisible in the fastest-growing discovery channel. Traditional analytics tools do not capture AI citation share. Brands are losing reach they cannot measure with standard dashboards.
Search
The foundation of the Presence Engine. Technical crawlability, entity authority, structured data markup, and topical depth that establishes the credibility signals AI systems require before citing a source. A brand that cannot be crawled cannot be cited. A brand without entity authority cannot be trusted by language models.
AI Search — Generative Engine Optimisation
The discipline of structuring content and brand signals so language models extract, cite, and recommend your brand when users ask relevant questions. GEO is not traditional SEO. It requires different content formats, different entity signals, and direct monitoring of AI output to know whether it is working.
Content (Quill)
One brand voice feeds all four surfaces: set once, carried unchanged across Local, Search, AI Search, and Social. One workflow for three engines, SEO, GEO, and Social, with content structured for AI extraction, not only human reading. No other platform writes in a single, locked brand voice across all four.
Social Signal
Cross-channel amplification that generates the engagement signals and third-party references AI systems use as authority indicators. Social is not separate from AI search — it is a primary signal source for it, reinforcing content authority in the training data that shapes AI citations.
Orchestration — Computation Mapping
Computation Mapping finds the keyword opportunities and routes them into the engine, where the fixes are written to the site for search and AI crawlers to read: a map that ends in action, not a spreadsheet. Without orchestration, four products; with it, one engine.
Why integration matters
A collection of five separate platforms — SEO tool, content tool, social scheduler, AI monitor, reporting dashboard — has no feedback mechanism between them. Each optimises for its own metric. There is no loop, and therefore no compounding. CLEO routes monitoring output directly into content creation. Published content triggers social amplification. Amplification results inform the next monitoring cycle. Authority accumulates with each iteration.
CLEO serves
Marketing leaders at established brands losing organic traffic to AI-generated answers. Growth teams that cannot manage five separate tools and still maintain a feedback loop. Brands with genuine expertise that is not reflected in their AI citation share. Enterprise teams needing dedicated stewardship, custom orchestration, and a long-term presence partnership.
AI citation share is not proportional to company size or marketing budget. It is proportional to how well a brand's content is structured for AI extraction and how consistently it publishes into its category. A twelve-person team can outperform a thirty-person team if the closed-loop system is in place. The brands building that system today are establishing an advantage that will compound for years.
The measurement framework
AI Readability Score (ARS) measures how extractable your website is to AI crawlers — scored across crawler access, JavaScript rendering, structured data, content quality, content size, and LLM accessibility. AI Visibility Score (GEO) measures how often your brand appears in AI-generated answers across ChatGPT, Perplexity, Google AI Overviews, and Claude. Infrastructure Readiness measures the technical baseline — robots.txt configuration, schema markup quality, Core Web Vitals, and indexability.
Getting started
The free Presence Scan at regencleo.ai/scan audits any domain across AI readability, AI answer visibility, and infrastructure readiness — no login required. Self-serve plans for independent teams beginning the work of compounding brand presence. Enterprise plans with dedicated account stewardship, custom workflows, and strategic partnership. Start the conversation at regencleo.ai/book.
