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.
By CLEO - RegenAI · Published · Updated
GEO score history - the lift, 44 to 63 within 30 days.
DisburseCloud is a cloud-based payment disbursement platform: businesses use it to send and automate outbound payments to vendors, agents, and claimants across multiple payment methods, built with the disposition of a small atelier. They had built a product their customers, carriers, MGAs, and TPAs, genuinely loved. The audience that should have known it, claims operations leaders at mid-market carriers, 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.
Method
Measured across four engines, ChatGPT, Google AI Overviews, Perplexity and Claude, monitored weekly over a ninety-day window, at 95% Wilson confidence. The prompt-set size and the sample count (n) behind the Wilson interval are not published for this engagement.
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 llms.txt protocol activated.
Social Signal listened across the five surfaces. The system drafted eighty-four responses in DisburseCloud's AI voice; the human team published seventy-one of them. Brand voice was tuned by ingesting the existing public communications corpus, the plain-spoken, declarative cadence 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 67% 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 platform scoreboard moved in step. Within the first thirty days, DisburseCloud's GEO Score climbed from 44 to 63, led by gains in share of voice, recommendation rate, and grounding frequency across the four models. Its SEO Score reached a full 100 out of 100, with zero outstanding issues across sixty-nine audited pages and clean site uptime, robots, and sitemap signals.
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 brought CLEO in to fix how we showed up in AI search, and our AI citation share went from 17% to 67% in ninety days. What I didn't expect was what came next. CLEO started turning that visibility into real leads, with names, companies and context, dropping straight into our pipeline. It's the first marketing platform our sales manager has ever asked to sit in on. That tells you everything."
DisburseCloud, a twelve-person fintech operating a cloud-based payment disbursement platform - businesses use it to send and automate outbound payments across eight payment modalities (virtual card, ACH, instant deposit, postal check, digital check, Venmo, PayPal, cash pickup) the recipient chooses from, plus vendor disbursements on the same platform. The company had strong technical credibility and a clear product, but was effectively invisible in AI-generated answers about outbound payment disbursement, payment disbursement platforms, and payee-choice payment modalities.
The challenge
competitors with larger marketing teams had established AI citation share earlier. When buyers asked ChatGPT or Perplexity about claims payment platforms, DisburseCloud 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. Alongside citation growth, the CLEO dashboard recorded DisburseCloud's GEO Score rising from 44 to 63 within thirty days - driven by gains in share of voice, recommendation rate, and grounding frequency - while its SEO Score reached 100 out of 100 with zero outstanding issues across sixty-nine audited pages.
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.
Method
measured across four engines, ChatGPT, Google AI Overviews, Perplexity and Claude, monitored weekly over a ninety-day window, at 95% Wilson confidence. The prompt-set size and the sample count (n) behind the Wilson interval are not published for this engagement.
In the client's words - Skip Gilleland, Chief Operating Officer, DisburseCloud: "We brought CLEO in to fix how we showed up in AI search, and our AI citation share went from 17% to 67% in ninety days. What I didn't expect was what came next. CLEO started turning that visibility into real leads, with names, companies and context, dropping straight into our pipeline. It's the first marketing platform our sales manager has ever asked to sit in on. That tells you everything."
Case study
The challenge
What CLEO did
Results after ninety days
The key insight
Method
Frequently asked questions
Who is DisburseCloud?
A twelve-person fintech operating a cloud-based payment disbursement platform. Businesses use it to send and automate outbound payments across eight payment modalities the recipient chooses from, plus vendor disbursements on the same platform.
What was the problem?
The company had strong technical credibility and a clear product but was effectively invisible in AI-generated answers about outbound payment disbursement. Competitors with larger marketing teams had established AI citation share earlier. When buyers asked ChatGPT or Perplexity about claims payment platforms, DisburseCloud did not appear, despite being a strong product with genuine market fit.
What did CLEO do?
A full presence audit covering AI Readability diagnostics, AI citation gap analysis, and competitive citation benchmarking. CLEO identified the queries where the brand should appear but did not, published AI-readable content targeting those gaps with schema markup and extraction-optimised formatting, monitored AI engine responses weekly, amplified content to generate third-party citation signals, and optimised each cycle on the observed citation changes.
What were the results after ninety days?
AI citation share rose from 17% to 67%, measured at 95% Wilson confidence, which is four times the citations. The brand moved from absent to the second most-cited source in its category for key payment infrastructure queries. Category-defining queries lifted from no rank to position three on average, and LinkedIn organic reach quadrupled without paid amplification. Within thirty days the GEO Score climbed from 44 to 63 and the SEO Score reached 100 out of 100 with zero outstanding issues across sixty-nine audited pages.
What does DisburseCloud say about the results?
Skip Gilleland, Chief Operating Officer of DisburseCloud, says AI citation share went from 17% to 67% in ninety days and that CLEO then began turning that visibility into real leads, with names, companies and context, landing directly in the pipeline, making it the first marketing platform his sales manager has asked to sit in on.
What is the method behind these figures?
Measured across four engines, ChatGPT, Google AI Overviews, Perplexity and Claude, monitored weekly over a ninety-day window, at 95% Wilson confidence. The prompt-set size and the sample count (n) behind the Wilson interval are not published for this engagement.
About CLEO by RegenAI
CLEO by RegenAI is the Presence Growth Engine, the world's first engine that runs a brand's entire presence, from the map to the model, autonomously, under your approval. Maps, Search, AI Search, Social and Reviews run in one closed loop, with the website as the foundation and every action routed into one approval queue. Evidence at regencleo.ai/worlds-first, and a free scan at regencleo.ai/scan.
