Customer Cases
How real enterprises improved their AI influence through the KHB platform — four anonymized cases spanning fintech, SaaS, healthcare, and education.
Why Case Studies Matter
AI influence is a new field. Case validation is the only honest evidence.
A New Discipline
AI influence is an emerging discipline. Unlike search engine optimization, which has twenty years of shared benchmarks, generative engine optimization lacks a common vocabulary, let alone a common measurement framework. When a CFO asks whether investing in AI visibility will produce a return, the only honest answer comes from outcomes already delivered. Case studies bridge that gap. They translate methodology into measurable shifts in how AI models describe, cite, and recommend a brand. Each case below documents the starting position, the engineering work performed, and the metric movement observed across multiple AI platforms over a fixed period. Without this evidence, claims about AI influence remain theoretical.
Transparency Commitment
Transparency is the foundation of this page. Every number reported here is traceable to a documented AI Checkup run, a content deployment log, and a measurement window shared with the customer. We do not report vanity metrics, projected scores, or unsupported growth figures. Every customer featured has authorized the publication of their anonymized case, signed a reference agreement, and reviewed the final wording. Where we have changed identifying details to protect commercial confidentiality, we say so explicitly. Where a result is mixed or partial, we report it as mixed. The goal is not to impress. The goal is to allow a prospective customer to evaluate whether the KHB platform fits their context.
Anonymization Principle
Our anonymization principle is simple: industries are real, company names are masked. A cross-border fintech appears as FinTech A, a B2B SaaS vendor as SaaS B, a medical device manufacturer as MedDevice C, and an education platform as EduTech D. The four cases span regulated finance, enterprise software, healthcare, and consumer education — four sectors where AI influence carries direct commercial weight. They were selected because each surfaced a different failure mode: invisibility, mischaracterization, factual error, and omission. Together they show that AI influence is not a single problem with a single fix, but a portfolio of issues requiring distinct engineering responses.
FinTech A — From invisible to competitive moat
Cross-border payments · 500+ employees · US$200M ARR
Challenge
FinTech A is a cross-border payments company headquartered in Singapore with more than 500 employees and approximately 200 million US dollars in annual revenue. Its customers are small and mid-sized enterprises moving money between Asian corridors. The marketing team had run paid search and content programs for years and assumed the brand was discoverable everywhere that mattered. A routine inquiry changed that assumption. When a prospective customer asked ChatGPT for the best cross-border payment provider for SMEs in Asia, FinTech A did not appear. The model recommended three competitors. The same pattern repeated across Claude, Gemini, and Perplexity. The brand was effectively invisible inside the fastest-growing discovery channel.
Solution
KHB began with an AI Checkup that produced three baseline scores: Visibility 28 out of 100, Citation 15 out of 100, Recommendation 8 out of 100. The diagnostic identified the root cause: insufficient structured, authoritative content on the specific intent queries that AI models surface when ranking payment providers. Visibility was low not because the brand was unknown, but because the brand had not been encoded into the sources AI models trust. KHB then deployed GEO Master, publishing twelve pieces of authority content — corridor guides, compliance explainers, fee transparency articles, and case data — across owned and partner domains over six months. Each piece was engineered for citation pickup, structured for retrieval, and tracked for model ingestion.
Results
The results were measured against the same eight-model panel used in the baseline. Visibility moved from 28 to 76, a 171 percent lift. Recommendation moved from 8 to 52, a 550 percent increase. The most consequential business outcome was commercial: inbound inquiries attributed to AI recommendations grew 340 percent over the measurement window. The chief marketing officer summarized the shift in one line: KHB turned our invisibility into a competitive moat. The case demonstrates that for payment infrastructure, AI visibility is now a category-entry requirement, not a nice-to-have.
"KHB turned our invisibility into a competitive moat." — CMO, FinTech A
SaaS B — From generic tool to category leader
Project management software · 120 employees · Series B
Challenge
SaaS B is a project management platform serving distributed engineering teams. The company has roughly 120 employees, recently closed a Series B round, and competes in a crowded category where differentiation is hard to articulate even in sales calls. The team's concern was not invisibility — the brand was mentioned by AI models — but mischaracterization. ChatGPT, Claude, and Gemini consistently described SaaS B as a general-purpose project management tool, interchangeable with a dozen peers. The differentiated capability that the team had built for distributed engineering workflows was being omitted from every AI summary. Prospects arrived with the wrong mental model, lengthening sales cycles and compressing win rates.
Solution
KHB paired the engagement around VIP Service, including a weekly AI Board meeting where the customer's product, marketing, and executive leads reviewed AI-generated summaries of their category in near real time. The AI Board surfaced the specific phrasing models used, the sources they cited, and the gaps between the brand's actual positioning and its AI-rendered positioning. In parallel, KHB authored and distributed a white paper formalizing the customer's methodology — the KAIF-aligned framework for distributed engineering coordination — and placed it with publisher partners whose content is heavily weighted by frontier models. The program ran for eight months with continuous measurement and quarterly recalibration.
Results
The Citation Index moved from 41 to 89, reflecting that authoritative sources now consistently described the platform's distributed-team specialization. More importantly, the language AI models used to describe SaaS B shifted from generic project management tool to leading tool for distributed teams. That phrasing change had direct commercial impact: enterprise sales cycles shortened by 35 percent because prospects entered the conversation already understanding the differentiation. The case shows that AI influence is not only about being mentioned — it is about being correctly characterized.
MedDevice C — Correcting AI's factual errors
Medical imaging devices · 2,000+ employees · Public company
Challenge
MedDevice C is a publicly listed manufacturer of medical imaging equipment with more than 2,000 employees and a global installed base. The challenge that brought them to KHB was unusual and high-stakes: AI models were describing their flagship device with factually incorrect specifications. When physicians, procurement teams, and clinical researchers asked ChatGPT or Claude about the device, the models reported wrong acquisition speeds, wrong reconstruction algorithms, and in some cases wrong clinical indications. These errors were not cosmetic. They were influencing clinical conversations and, in a small number of documented cases, procurement shortlists. The regulatory team classified the issue as a material risk.
Solution
KHB's AI Checkup confirmed the scope: three of the surveyed AI models contained materially incorrect descriptions of the device, and the error rate across a structured query set was 47 percent. Root-cause analysis traced the errors to a small number of outdated and mis-cited third-party review sites that had been ingested as authoritative sources. KHB deployed Authority Engineering, partnering with five medical media outlets and three peer-reviewed clinical publications to publish corrected, current, and properly sourced technical specifications. Each partner publication was selected for its citation weight in the models' training and retrieval corpora. The deployment window was four months, with weekly progress audits shared with the customer's regulatory and legal teams.
Results
The results were measured against the same query set. Error rate dropped from 47 percent to 4 percent. AI recommendation accuracy for the device improved by 89 percent, meaning the models now correctly described acquisition speed, reconstruction method, and clinical indications. Commercially, the sales team reported a 60 percent increase in physician-initiated inquiries that referenced accurate specifications. The case demonstrates that for regulated industries, AI influence work is not marketing optimization — it is corrective infrastructure that protects clinical accuracy and commercial trust.
EduTech D — Winning the AI recommendation layer
Online learning platform · 300 employees · Unicorn
Challenge
EduTech D is an online learning platform serving K-12 and higher education markets. The company has roughly 300 employees, has achieved unicorn valuation, and competes against well-funded incumbents with strong brand recognition. The problem that surfaced during a market review was specific and damaging: when students, parents, and teachers asked AI assistants to recommend online learning platforms, the assistants consistently named three competitors and omitted EduTech D entirely. The brand had invested heavily in performance marketing and SEO, but those channels did not translate into AI visibility. The team recognized that AI recommendation was becoming the default discovery layer for education decisions and that invisibility there would compound over time.
Solution
KHB established an AI Board to build a continuous monitoring mechanism, tracking how AI models described the category across student, parent, and teacher query scenarios. Each audience uses different language, weights different criteria, and consults different sources, and the monitoring surfaced that the brand was absent from all three. KHB then engineered a content matrix that mapped each audience's query intent to a specific authority asset: comparison content for students, outcome and accreditation content for parents, and pedagogy and curriculum content for teachers. The five-month deployment included owned content, partner placements, and structured data enhancements to ensure each asset was retrievable by AI models.
Results
Visibility moved from 52 to 91. For the high-intent query best online learning platforms, EduTech D entered the top three AI recommendations across the surveyed models, where it had previously been absent. Organic traffic from AI-assisted discovery grew 280 percent over the measurement window. The case shows that AI influence in consumer education markets is a function of audience-specific authority engineering, not generic brand awareness. Winning the AI recommendation layer requires understanding the distinct intent of each audience and building the authoritative assets that answer it.
Results Framework
Common patterns across all four cases — and what they mean for your engagement.
Diagnose → Engineer → Sustain
Across the four cases a common pattern emerges. Every engagement followed a three-phase rhythm: Diagnose, Engineer, and Sustain. The Diagnose phase established a baseline through AI Checkup, identified the specific failure mode — invisibility, mischaracterization, factual error, or omission — and quantified the gap. The Engineer phase deployed the relevant KHB module: GEO Master for content authority, VIP Service for ongoing AI Board governance, or Authority Engineering for corrective work in regulated categories. The Sustain phase maintained measurement, recalibrated against model updates, and protected the gains. No case was a one-shot campaign. AI influence requires continuous engineering because the models themselves change continuously.
Time to Result
Time-to-result followed a consistent shape. Initial movement appeared within three to six months as new authority content was ingested and surfaced by models. Consolidation — the period during which gains became stable across model updates — took six to twelve months. Industry differences were real and predictable. Financial services moved fastest because the category's authority sources are well-defined and quickly indexed. Healthcare moved slowest because of regulatory constraints and the longer ingestion cycles of clinical publications. Education sat in the middle, with content authority compounding steadily over the deployment window. These timelines should be treated as planning anchors, not guarantees.
Return on Investment
On investment return, every case exceeded a 5x multiple when measured against the commercial outcomes most relevant to the customer — inbound inquiries, sales cycle compression, or corrected recommendation accuracy. The most reliable returns came from engagements where the customer committed to the full Diagnose-Engineer-Sustain cycle rather than treating AI influence as a one-time project. If you want to understand which pattern fits your context, book an advisory session and we will walk through it.
Frequently Asked Questions
Answers to the questions prospects ask most often.
How are case studies verified?
Every case study is built from three documented sources: a baseline AI Checkup run shared with and reviewed by the customer, a content deployment log recording what was published and when, and a measurement report tracking model responses across the engagement window. Customers review and sign off on the final wording before publication. Where identifying details have been changed to protect confidentiality, the changes are noted. We do not publish projected or hypothetical results.
What industries does KHB serve best?
KHB's strongest fit is in categories where AI recommendations carry direct commercial weight: financial services, B2B SaaS, healthcare and medical devices, education, and regulated professional services. These are sectors where a wrong AI description or an absent AI recommendation translates into lost pipeline, delayed procurement, or clinical risk. We have also worked with consumer brands, industrial manufacturers, and professional services firms. The common thread is that the customer's buyers consult AI models during their decision process. If your buyers do, KHB is relevant.
How long until we see results?
Initial movement typically appears within three to six months as new authority content is ingested by AI models. Consolidation — gains that hold across model updates — takes six to twelve months. Healthcare engagements tend to run longer due to regulatory and publication cycles. Financial services engagements often move faster. We provide a phased forecast at the start of every engagement and update it monthly based on observed model behavior. AI influence is not a sprint; it is sustained engineering work.
Can we talk to existing customers?
Yes, subject to mutual confidentiality. Several customers have agreed to participate in reference calls with qualified prospects. We arrange these introductions after an initial advisory session confirms that the prospect's context is a reasonable match. The reference conversations are candid — customers discuss what worked, what was harder than expected, and what they would do differently. We do not script them. If a reference call is important to your evaluation, raise it during the advisory session.
What is the typical investment range?
Engagements range from a free AI Checkup to six-figure annual programs for enterprise customers running GEO Master, VIP Service, and AI Board in parallel. Most paying customers fall between 60,000 and 250,000 US dollars per year depending on scope, number of markets, and the depth of authority engineering required. Healthcare and regulated engagements sit at the higher end due to compliance overhead. We provide a specific quote after the diagnostic phase, once the failure mode and required engineering scope are clear.