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

Data and AI infrastructure stack coverage A vertical stack diagram showing the data and AI infrastructure layers from bottom to top: storage and compute foundation, distributed databases, data integration and observability, graph databases, ML platforms and MLOps, and AI applications at the top. Verto-served clients are tagged at each layer. 07 · AI APPLICATIONS AI / NLP platforms Expert.ai 06 · ML PLATFORMS / MLOPS Data science platforms Domino Data Lab 05 · AI INFRASTRUCTURE / HPC High-perf storage / parallel FS 04 · GRAPH DATABASES Knowledge / relationship data Neo4j 03 · OBSERVABILITY / TELEMETRY Logs · metrics · traces Cribl 02 · INTEGRATION / IPAAS Data + app integration SnapLogic 01 · DISTRIBUTED DATABASES Cloud-native storage Across every layer: developer awareness SEQUENCED INTO ENTERPRISE PIPELINE

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

PLG-to-enterprise funnel A three-stage funnel narrowing from top to bottom. Top: developer and engineer awareness via Stack Overflow, GitHub, technical blog content, LinkedIn, and AI search citations. Middle: technical evaluation including POC, benchmarks, and integration testing. Bottom: enterprise procurement covering pricing, security review, and multi-year contracts. 01 · DEVELOPER AWARENESS Developer / engineer interest STACK OVERFLOW · GITHUB · TECHNICAL BLOG LINKEDIN · AI SEARCH CITATIONS 02 · TECHNICAL EVALUATION POC · benchmarks · integration testing DATA LEADERSHIP REVIEWS · COST/SCALE MODELLING DOCUMENTATION DEPTH · API ERGONOMICS 03 · ENTERPRISE PROCUREMENT Pricing · security review · multi-year contract CTO · VP DATA · SECURITY ARCHITECT · CFO CONTRACT · SUPPORT TIER · COMPLIANCE Marketing's role: optimize the conversion between these layers · not for one of them in isolation.
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

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 LinkedInPaid Social PaidSearch 6sense /DemandBase LinkedInAI Targeting Contact-Level ABM PipelineIntelligence
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

Outbound

Explore Outbound

Data & Analytics

Inside Pipeline Intelligence

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
Cribl

$100M

ARR contribution

2×

content production

Hybrid AI + human content engine, full technical SEO foundation, AEO authority program.

See the work
SnapLogic

+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
Neo4j

+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

Customer voice

Our clients speak for themselves.

G2

4.9 / 5

"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

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

Mickey Hsieh

Sr. Web Marketing Manager, Cribl

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