Clay is a B2B data and automation platform that lets you build 'tableaus' combining 100+ data sources, web scraping, and AI to research prospects and automate outbound at scale. A Clay workflow typically: builds a target account list, enriches it with company data, finds contacts, waterfalls through email providers, uses AI to generate personalization, and pushes records to your sequencing tool. It's become the de facto outbound enrichment infrastructure for AI-native GTM teams.
For example, a Clay workflow for an ABM campaign might: pull companies that match ICP from Apollo → enrich with funding data from Crunchbase → find VP-level contacts → generate personalized icebreakers using AI based on recent LinkedIn activity → export to Outreach with all fields populated.
Clay is central to our outbound infrastructure — we've built dozens of client workflows and maintain internal templates for common GTM motions.
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We implement Clay Workflow strategies for B2B tech startups every day. Book a free 30-minute call to get a concrete plan for your situation.
Book a free strategy call →Agentic AI
Agentic AI refers to AI systems that can plan, take actions, use tools, and complete multi-step tasks autonomously — going beyond generating text to actually doing work.
AI Agent
An AI agent is an LLM-powered system that can autonomously use tools, access data, and complete tasks — as opposed to a simple chatbot that only responds to single prompts.
Autonomous Workflow
An autonomous workflow is a multi-step automated process that runs without human intervention — trigger, conditions, actions, branches, and loops all executing on schedule or in response to events.
Human-in-the-Loop (HITL)
Human-in-the-loop describes AI automation workflows that include a human review or approval step before consequential actions are taken — particularly sending outreach, making calls, or publishing content.
Large Language Model (LLM)
An LLM is the AI model underlying most modern AI tools — GPT-4, Claude, Gemini, Llama.
Prompt Engineering
Prompt engineering is the practice of designing inputs to AI models to get better, more consistent outputs.