--- title: "Enterprise Communications Platform AI Search Case Study" canonical: https://snezzi.com/case-studies/enterprise-communications-platform-case-study/ source: https://snezzi.com/case-studies/enterprise-communications-platform-case-study/ --- > Canonical page: https://snezzi.com/case-studies/enterprise-communications-platform-case-study/ Customer context ## About Confidential YC-Backed Comms Platform Confidential YC-Backed Comms Platform is a enterprise saas - communications (regulated) company based in United States. This record covers the baseline and scope described below; it does not imply that every company begins from the same conditions. [Visit company website](#) Industry Enterprise SaaS - Communications (Regulated) Location United States Founded 2011 Team 200-300 Starting problem ## Invisible in AI search, in a category where every claim must clear compliance A YC-backed enterprise communications platform, US-based with roughly $85M in annual revenue and tens of thousands of business customers including global enterprise logos, runs programmable voice and messaging at enterprise scale, now extending into AI voice agents. Its buyers research vendors inside ChatGPT, Perplexity, and Google AI, but the brand was largely absent for the questions that matter: building AI voice agents, HIPAA-safe patient messaging, replacing legacy IVR. The hard part is the category. Operating under HIPAA, SOC 2, and ISO 27001, the brand cannot simply publish to win AI citations, every claim has to survive compliance review, which is exactly where most content programs stall. - Operating under HIPAA, SOC 2, and ISO 27001, every published claim must clear review - Largely absent from AI answers on AI-voice, HIPAA-safe messaging, and IVR-replacement queries - 422 legacy URLs not earning citations; only 7 clear winners - Compliance review stalling the content velocity needed to compete in AI search Work delivered ## Compliance-aware AEO that earns citations and survives review We built an AEO program engineered for a regulated category. Every draft is written and reviewed against HIPAA, SOC 2, and ISO 27001 sensitivities with reviewer sign-off, so claims are citable and safe, the difference between content AI will quote and content a compliance team kills. We mapped a buyer-query universe of roughly 100 prompts across four pillars (AI Voice Agents, Healthcare, Customer Service, Education), re-evaluated monthly against visibility, and audited the full 422-URL legacy footprint to concentrate authority where AI can cite it. 1. 01 ### Compliance-Aware Content Standard Every draft reviewed against HIPAA, SOC 2, and ISO 27001 sensitivities with reviewer sign-off built into the workflow, so claims are safe to publish and credible enough for AI to cite. 2. 02 ### 422-URL Audit + Authority Concentration Classified the full footprint (7 winners, 72 page-2, 149 to retitle, 194 to prune or consolidate) so crawl budget and authority concentrate on citable assets instead of an aging long tail. 3. 03 ### Pillar Architecture for AI Buyers AI Voice Agents, Healthcare, Customer Service, and Education hubs mapped to a buyer-query universe of roughly 100 prompts, re-evaluated monthly against visibility. 4. 04 ### Fast-Win Targeting Prioritized low-difficulty, real-demand queries (e.g. 'AI outbound calls', KD 3, 210/mo) to earn early citations while the larger regulated hubs clear review and ship. Methodology ## How the work progressed The sequence and durations below come from this engagement record. 1. Phase 1 · 3 weeks ### Footprint Audit + Compliance Mapping Crawled and classified all 422 URLs and set the HIPAA/SOC2/ISO review bar every new and existing asset has to clear. 2. Phase 2 · 3 weeks ### Pillar Architecture + Prompt Mapping Designed the four-pillar hub-and-spoke and mapped a buyer-query universe of roughly 100 prompts to it. 3. Phase 3 · Ongoing ### Compliance-Aware Production Producing spokes under a HIPAA/SOC2/ISO-aware review workflow with reviewer sign-off, starting with fast-win queries. 4. Phase 4 · Ongoing ### Concentrate Authority + Re-Evaluate Monthly Executing the prune/retitle plan and re-evaluating the prompt set monthly against visibility as compliant content ships. Reported for this engagement ## Measured movement over In progress (engagement active) These values are specific to Enterprise Communications Platform's baseline, scope and measurement period. They are evidence of this engagement, not a forecast or guarantee for another brand. Compliance Standard HIPAA + SOC 2 + ISO Content standard with reviewer sign-off, so every published claim is safe to ship and credible enough for AI to cite. URLs Audited & Triaged 422 Full legacy footprint classified to concentrate authority where AI can cite it. Buyer Prompts Mapped ~100 Buyer-query universe across four pillars, re-evaluated monthly against visibility. Legacy Pages Retired 194 Pages slated to prune or consolidate so they stop diluting authority. > “In our category you cannot just publish, every claim has to clear compliance first. Snezzi built AEO content that earns citations and survives that review, which is something nobody else we spoke to could actually do.” What this suggests ## Lessons from the engagement 1. 01 In a regulated category, the constraint is not ideas, it is approval. AEO only works if the content can clear HIPAA, SOC 2, and ISO review. 2. 02 A compliance-aware content standard is an advantage: AI engines cite sources whose claims hold up, and reviewers stop blocking the pipeline. 3. 03 For an established brand, the first AEO win is often subtraction, pruning low-value pages so authority concentrates where AI can cite it. 4. 04 Fast-win, low-difficulty queries earn early citations while the high-value regulated hubs clear review and ship. More customer evidence ## Related case studies [View all case studies](/case-studies/) [![Enterprise AI-Agent Platform case study](/assets/case-studies/snezzi_ai_saas_casestudy.png)](/case-studies/ai-saas-sales-support-case-study/) ![Enterprise AI-Agent Platform logo](/assets/case-studies/ai_saas.png) Enterprise AI - Agentic Platforms From AI-search invisible to the cited answer, through a high-stakes migration ## [Enterprise AI-Agent Platform](/case-studies/ai-saas-sales-support-case-study/) A Series-A enterprise AI-agent company, US-headquartered with an India R&D base and roughly $42M raised from top-tier funds, builds voice and chat agents that handle 250,000+ live customer conversations a month for enterprises in insurance, travel, and fintech. The product was strong and the logos were real. But the buying committee (VPs of CX, contact-center heads, AI/ML platform leads) had moved vendor research into ChatGPT, Perplexity, and Google AI. For queries like 'best enterprise AI agent platforms', 'voice AI vendor comparison', and 'AI agents to replace BPO', the company scored 0% AI visibility. To compound it, the brand was mid-flight on a full website migration that risked erasing the limited authority it already had. AI Visibility 0 to 34.5% AI Citations 7,733 [Read case study](/case-studies/ai-saas-sales-support-case-study/) [![Fintech Engineering Firm case study](/assets/case-studies/snezzi_ai_saas_casestudy.png)](/case-studies/fintech-engineering-authority-case-study/) ![Fintech Engineering Firm logo](/assets/case-studies/ai_saas.png) Financial Services - Engineering & Platforms Building category authority in regulated finance, from near-zero ## [Fintech Engineering Firm](/case-studies/fintech-engineering-authority-case-study/) An established, privately-held engineering firm builds AI-native platforms and governed AI systems for regulated financial-services clients across the UK, EU, and US, the kind of work that wins credit-decisioning and economic-crime mandates at major banks. Its expertise was real and its references blue-chip, but in AI search it barely existed: 1.5% AI visibility, barely a prompt surfacing a mention, and almost no citations. For a firm whose entire growth thesis is thought leadership and becoming a recognized name in the category, being absent from the AI answer layer was a serious gap. AI Visibility 1.5% to 4% AI Citations 11 [Read case study](/case-studies/fintech-engineering-authority-case-study/) Start from your own data ## Establish your AI-search and organic visibility baseline See your current position in the platform and discuss human and agent execution when you want Snezzi to own the work. [Book a demo](/strategy-session/) [View pricing](/pricing/)