Clay Core Workflow B: Claygent AI Research & Personalization

Overview

Complements the enrichment pipeline (clay-core-workflow-a) with AI-powered research and personalization. Uses Claygent (Clay's built-in AI research agent powered by GPT-4) to scrape websites, extract insights, and generate personalized outreach copy for each prospect. 30% of Clay customers use Claygent daily, generating 500K+ research tasks per day.

Prerequisites

• Completed clay-core-workflow-a with enriched table
• Clay Pro plan or higher (Claygent requires Pro+)
• Understanding of prompt engineering basics

Instructions

Step 1: Add a Claygent Research Column

In your Clay table with enriched leads:

1. Click + Add Column > Use AI (Claygent)
2. Choose model: Claygent Neon (best for data extraction and formatting)
3. Write your research prompt referencing table columns:

Research {{Company Name}} ({{domain}}) and find:
1. Their most recent funding round (amount, date, investors)
2. Any recent product launches or major announcements from the last 6 months
3. Their primary competitors

Return results as structured data. If information is not found, return "Not found" for that field.

1. Enable Auto-run on new rows

Step 2: Configure Multi-Output Claygent (Neon Model)

Claygent Neon can extract multiple data points into separate columns from a single run:

Research the company at {{domain}} and extract:

Output 1 (Recent News): The most notable company news from the last 90 days. One sentence.
Output 2 (Tech Stack): List the main technologies they use (check job postings, BuiltWith, Wappalyzer data).
Output 3 (Pain Points): Based on their Glassdoor reviews and recent job postings, identify likely operational pain points.
Output 4 (Competitor): Name their primary competitor.

Map each output to a separate column for downstream use in personalization.