--- title: "Data Platforms & AI Infrastructure Marketing | VertoDigital" description: "B2B marketing agency for data infrastructure and AI companies. iPaaS, observability, graph databases, ML platforms, AI infra. Cribl, SnapLogic, Neo4j." url: "https://vertodigital.com/industries/data-platforms-and-ai-infrastructure-marketing" image: "https://vertodigital.com/images/site/og-default.png" --- Industries · Data & AI Infrastructure # Marketing for the data and AI infrastructure stack. Data platforms and AI infrastructure don't sell like B2B SaaS. The user is a developer. The buyer is a CTO or VP of Data. The procurement gate is a security architect. Marketing has to shape three audiences on three timelines - and tie all of it back to enterprise pipeline. We run pipeline programs across the full data stack: iPaaS, observability, data warehouses, graph databases, ML platforms, AI infrastructure. Cribl, SnapLogic, Neo4j, and Domino Data Lab. The buying committee spans developer, CTO, and security architect. We map all three. iPaaS · observability · graph · ML · AI infra Developer awareness → enterprise pipeline AI search citation authority Where we've worked ## The categories we've operated across. The data and AI infrastructure stack isn't one category. Each layer has different buyer dynamics, different competitive frames, and different content motions that work. Data + AI stack · Verto coverage Integration & iPaaS · SnapLogic ICP-fit campaigns into integration-led buyers. 125% increase in Stage 1 opportunities through value-based bidding. Observability & telemetry · Cribl SEO + AEO content authority program. 95% YoY organic growth. Contribution to $100M ARR. Graph databases · Neo4j Developer awareness sequenced into enterprise procurement. 157% increase in net-new enterprise leads. ML platforms & MLOps · Domino Data Lab Long-cycle nurture of data science buyers + procurement-stage proof points. Distributed databases & cloud-native data Multi-region, multi-cloud campaign infrastructure. AI infrastructure & high-performance storage Enterprise AI workload buyer engagement. AI application platforms · Expert.ai Specialized B2B AI category positioning. What's different ## What makes data and AI infrastructure different. Three dynamics shape every pipeline program in this vertical. Live · PLG-to-enterprise funnel developer signal → enterprise pipeline 01 PLG signal vs pipeline signal ### The developer is the user, not the buyer. Open-source adoption, free-tier signups, GitHub stars - these are signals of developer interest, not enterprise pipeline. Marketing's job is to shape the developer awareness layer in a way that eventually compounds into enterprise procurement. Most agencies optimize for one or the other. Pipeline-driven data marketing optimizes for the conversion between them. 02 Three audiences · one deal ### The buying committee spans three audiences. Developers care about API design, documentation depth, integration ergonomics. Data leadership cares about scale, governance, total cost of ownership. Procurement cares about contracts, support tiers, security certifications. Each audience needs different content, different channels, different proof points. The deal closes when all three converge. 03 AI search · category visibility ### AI search is changing how technical buyers research. Developers and data engineers increasingly start their evaluation in ChatGPT or Perplexity, not in Google. The companies that establish citation authority in AI search results now will own the next decade of category visibility. Most data infrastructure companies are invisible there. 04 Foundation ### Documentation is marketing. The best-performing data infrastructure SEO content isn't blog posts - it's documentation written for both crawlability and citation. Technical SEO and AEO sit at the foundation of every data-vertical engagement we run. [Inside SEO & AEO](https://vertodigital.com/services/inbound/b2b-seo-and-aeo) Services · Data / AI GTM mapping ## What we run for data and AI infrastructure companies. The Verto inbound and outbound streams map to data-vertical GTM. The full system runs on every engagement; specific layers get weighted by stage and motion. Service · Data + AI GTM motion · matrix Data / AI motion ↓ · Service → SEO / AEO LinkedIn Paid Social Paid Search 6sense / DemandBase LinkedIn AI Targeting Contact -Level ABM Pipeline Intelligence Developer awareness Documentation-led organic Data-leadership engagement Enterprise procurement Customer expansion Usage-signal attribution Primary Supporting Not used PLG-led ### Developer-led, freemium / open-source / free tier. - Heavy SEO + AEO investment - the documentation and technical content layer is the awareness motion - LinkedIn awareness sequenced for the data-leadership conversion (developer → VP of Data → enterprise pipeline) - Light paid search initially; scale once usage signal is rich enough to feed value-based bidding Sales-led enterprise ### Top-down, named-account. - Full inbound stream + 6sense / DemandBase intent intelligence - LinkedIn AI Targeting layered on the data-leadership and platform-architect personas - Contact-Level ABM on enterprise targets - Pipeline Intelligence ties product usage signal into the bid algorithm Hybrid · most common ### PLG + enterprise. - Coordinated motion across both - developer awareness via SEO/AEO and technical content; enterprise pipeline via LinkedIn ABM and contact-level outreach - Pipeline Intelligence is critical - usage signal from the product flows back into ad platforms as conversion data, so the algorithm learns which developers convert into enterprise pipeline [Inbound Explore Inbound](https://vertodigital.com/services/b2b-inbound-pipeline-growth-and-demand-generation) [Outbound Explore Outbound](https://vertodigital.com/services/b2b-outbound-pipeline-growth-and-demand-generation) [Data & Analytics Inside Pipeline Intelligence](https://vertodigital.com/services/b2b-pipeline-intelligence-and-marketing-attribution) What this has looked like ## The work, in data and AI infrastructure. A few engagements that show how the system adapts across the stack. [See all case studies](https://vertodigital.com/case-studies#data-platforms-ai) [$100M ARR contribution 2× content production Hybrid AI + human content engine, full technical SEO foundation, AEO authority program. See the work](https://vertodigital.com/case-studies/cribl-100m-arr) [+125% Stage 1 opportunities 78% of opportunities showed LinkedIn influence Value-based bidding tied to CRM-defined ICP signals + LinkedIn AI Prospecting Agent. See the work](https://vertodigital.com/case-studies/snaplogic-troas) [+157% net-new enterprise leads ML audience expansion developer awareness → enterprise pipeline "VertoDigital helped us tap into machine learning to find new customer prospects." (Lauren McCormack, Neo4j.) See the work](https://vertodigital.com/case-studies/neo4j) Customer voice ## Our clients speak for themselves. [G2 4.9 / 5](https://www.g2.com/products/vertodigital/reviews) > "VertoDigital helped us tap into the power of machine learning to find new customer prospects and grow our high-performing audience segments to drive ROI." Lauren McCormack Senior Manager, Digital and Marketing Automation, Neo4j > "VertoDigital's hybrid content approach was the perfect fit. By combining the efficiency of AI with the expertise of human editors, we've been able to produce high-quality content at a faster pace. This has not only boosted our organic traffic by 35% but has also strengthened our brand's authority in the industry." Mickey Hsieh Sr. Web Marketing Manager, Cribl Frequently asked ## Questions we get from data and AI marketing leaders. “How do you reach enterprise buyers in data and AI infrastructure?” The buying committee for data infrastructure typically spans engineering leadership (VP of Engineering, data platform lead), IT procurement, and the CFO office as spend scales. LinkedIn AI targeting reaches these titles precisely by seniority and company firmographics. Paid search captures the architectural and evaluation queries. SEO and AEO earn visibility in the AI search answers your buyers read during vendor evaluation. “How do you balance developer-led growth and enterprise sales in the same programme?” PLG awareness and enterprise pipeline run as separate campaign architectures - different keywords, different LinkedIn audience signals, different conversion goals - then both feed into a single attribution model in the CRM. That’s how you can see which developer-led signals are converting to enterprise pipeline, and optimise accordingly. “What pipeline results have you seen with data and AI infrastructure companies?” Cribl’s content programme contributed to their $100M ARR milestone. Neo4j saw 157% more enterprise leads at 54% lower cost per acquisition. SnapLogic saw a 125% increase in Stage 1 opportunities with 78% of those opportunities showing LinkedIn influence. Results vary by starting state - the 90-day pilot is designed to establish a measurable pipeline baseline by day 90. Start here ## See whether developer awareness is converting to pipeline. In five days. A free Pipeline Readiness Assessment for data and AI infrastructure companies. An ICP audience snapshot, an AI search visibility check across category queries, and a measurement gap analysis tied to product-usage signals - so you can see exactly where developer interest is or isn't becoming enterprise pipeline. Yours to keep. Five days. We map developer signal to pipeline. Yours to keep. [Get Your Free Pipeline Assessment](https://vertodigital.com/assessment)