How do you find AI infrastructure buyer contacts in Clay?

Connect Apollo, LinkedIn, and Crunchbase to Clay and filter by job titles including VP AI Engineering, Head of ML Platform, Director AI Infrastructure, Head of MLOps, and Principal ML Engineer. Layer in company signals like recent AI funding, active ML job postings, and AI product announcements.

What are the best buying signals for AI infrastructure outbound?

Top signals are: active job postings for MLOps or AI Infrastructure roles (scaling signal), recent AI product launch or announcement, executive LinkedIn posts discussing model cost or latency challenges, and recent funding rounds for AI-native companies.

How do you personalize outreach for AI infrastructure buyers?

Reference their specific model provider (Claude, GPT-5.5, Gemini, or open models), their infrastructure scale, and the specific challenge they are publicly discussing. Frame it as an event invite for a peer conversation, not a product demo request.

What events convert AI infrastructure buyers?

Technical working sessions on specific infrastructure problems (model cost optimization, AI security, latency reduction at scale, AI governance) with credible peer speakers from comparable companies. Vendor-heavy webinars perform poorly with this persona.

What is Stage 3 in AI infrastructure buying and why does it matter for outbound?

Stage 3 accounts have an existing AI infrastructure team and are publicly discussing challenges at scale -- cost, latency, compliance. These are the highest-converting accounts for outbound because they have defined problems and allocated budget. Stage 1 and 2 accounts are better served by content rather than direct outreach.

Building an AI Infrastructure Outbound Campaign with Clay in 2026

Why AI Infrastructure Is the Hottest Outbound Category in 2026

Meta is spending $115-135 billion on AI infrastructure this year. Google, Microsoft, and Amazon are each committing comparable amounts. Across thousands of smaller AI-native and AI-adopting companies, infrastructure spend is growing at 40%+ year over year.

The buyers making those decisions -- Heads of ML Platform, VP of AI Engineering, Director of AI Infrastructure -- are not responding to generic sales outreach. They are technical, skeptical of vendor claims, and inundated with AI vendor pitches.

Clay lets you build a campaign that demonstrates you understand their specific infrastructure stack and the specific decisions they are navigating.

Step 1: Define the AI Infrastructure ICP

AI infrastructure buyers span several segments. In Clay, build separate tables for each:

Large AI-native companies (500+ employees): Companies that have built AI as a core product function. They need MLOps, model management, AI security, compute cost optimization, and data pipeline tooling. Find them via Crunchbase AI company filters and LinkedIn company type.

Enterprise AI adopters (1,000+ employees): Traditional enterprises actively building AI capabilities internally. They need AI governance, model deployment infrastructure, and integration tooling. Job postings for "ML Platform Engineer" or "AI Infrastructure" signal this segment.

AI infrastructure vendors themselves: Companies building AI chips, training infrastructure, or model serving platforms are simultaneously buyers of adjacent tooling (observability, security, compliance).

Target titles: VP AI Engineering, Head of ML Platform, Director AI Infrastructure, Head of MLOps, Principal ML Engineer, VP Platform Engineering.

Step 2: Enrich with AI Infrastructure Buying Signals in Clay

In Clay, add enrichment columns:

Job posting enrichment: Companies posting for ML Platform, MLOps, or AI Infrastructure roles in the last 30 days are actively scaling their AI stack. These are your hottest accounts.

Model provider detection: Use Claygent (Clay's AI agent) to identify which AI models the company is using or building on (Claude, GPT-5.5, Gemini, open models). This helps you understand their stack and personalize accordingly.

Funding and capex signals: Recent funding rounds for AI-native companies or AI infrastructure division announcements from enterprises signal active budget.

LinkedIn engineering posts: Executives and engineers publicly discussing AI infrastructure challenges (model latency, cost, compliance) are signaling pain points. Clay can pull recent LinkedIn activity for your target contacts.

Step 3: Score by Infrastructure Decision Stage

AI infrastructure deals happen in phases. Score your Clay list by likely decision stage:

Stage 1 -- Exploration (score 1): Just hired AI infrastructure leaders, no public AI product yet. Stage 2 -- Building (score 2-3): Job postings for MLOps or model deployment roles, recent AI product announcements. Stage 3 -- Scaling (score 4-5): Existing AI infrastructure team, public discussion of cost, latency, or compliance challenges at scale.

Focus outbound on Stage 3 accounts. They have a defined problem and budget. Stage 1 and 2 accounts are better served by content marketing and event awareness.

Step 4: Build the Event Invite -- Not the Demo Request

AI infrastructure buyers at Stage 3 do not want a demo. They want peer conversations about the problems they are actually solving.

Use Clay's AI column to generate event invite first lines that reference:

The specific model provider they are using or building on
The infrastructure scale they are operating at (inferred from team size and public data)
The specific topic of your event ("model cost optimization at scale", "AI governance for enterprise", "security for AI agents")

Template: "Given [Company]'s work on [specific AI initiative], we're hosting a working session on [specific AI infra topic] with [credibility signal] on [date]. Would it be relevant?"

Step 5: Sequence and Track in Clay + Apollo

Build a 5-step Clay + Apollo sequence:

Email with event invite (Day 1)
LinkedIn connection from a named engineer or leader (Day 3)
Follow-up email with one relevant data point (Day 7)
LinkedIn message referencing the specific infrastructure topic (Day 10)
Breakup email (Day 14)

Track reply rate, event registration, and meeting booking by account segment and signal score. The scoring model will sharpen over 3-4 campaigns.