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AI-Powered GTM Automation

Unlock GTM alpha with AI workflows that save time, reduce costs, and drive conversion

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How AI is Reshaping Go-to-Market Strategy
0
How AI is Reshaping Go-to-Market Strategy
GTM's AI Toolbox: The FETC Framework
1
GTM's AI Toolbox: The FETC Framework
Best Practices To Maximize Your Credits
2
Best Practices To Maximize Your Credits
Find: AI-Driven Lead Discovery
3
Find: AI-Driven Lead Discovery
Find Your ICP with AI
4
Find Your ICP with AI
Find Your Next Customer with Company Lookalikes
5
Find Your Next Customer with Company Lookalikes
Enrich: Build Complete Prospect Profiles with AI
6
Enrich: Build Complete Prospect Profiles with AI
Enrich with Claygent for Last-Mile Data Discovery
7
Enrich with Claygent for Last-Mile Data Discovery
Enrich with Perplexity
8
Enrich with Perplexity
Enrich Images and Screenshots
9
Enrich Images and Screenshots
Enrich from Financial Filings
10
Enrich from Financial Filings
Transform: Clean, Structure, and Segment Data with AI
11
Transform: Clean, Structure, and Segment Data with AI
Transform with AI Formulas (and Optimize Credits)
12
Transform with AI Formulas (and Optimize Credits)
Transform Data Into Clear Classifications
13
Transform Data Into Clear Classifications
Create: AI-Driven Effortless Output
14
Create: AI-Driven Effortless Output
Create Personalized Content at Scale
15
Create Personalized Content at Scale
Create Multi-Step Sequence Messaging with Twain
16
Create Multi-Step Sequence Messaging with Twain
AI-Powered GTM is Constantly Evolving
17
AI-Powered GTM is Constantly Evolving

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Enrich with Claygent for Last-Mile Data Discovery
About this lesson
00:00

First up in the Enrich section, we're going to cover Claygent, Clay's Agentic AI scraper.

Claygent answers the age-old question - "What happens when all your providers run, and you still don't have what you need?"

That's what we call the last-mile data problem.

🔍 Understanding the Last-Mile Data Problem

With Clay waterfalls, you already have a huge advantage when it comes to enrichment. When you're intelligently gathering data from 120+ providers instead of a few pieced together sources, data coverage goes up to 80-95%.

But even with the perfect waterfall setup—with every provider firing—you'll still run into a last-mile data problem.

Last-mile data is the data no provider can give you. These are the real, tangible gaps in your CRM that teams have had to historically assign for manual collection from BDRs. For example:

  • Whether a local dentist offers Invisalign
  • If a founder spoke at a panel last month
  • Or what a company's website says about pricing or features

The defining characteristic of last-mile data is that it's custom data that matters to your ICP and process, but too niche for a data provider to collect.

No wonder the average SDR spends 80% of their time on data entry and cleaning!

🔧 How Claygent Works

With Claygent, you can automate this last step of research. Claygent acts like a virtual research assistant. Its cutting-edge agentic architecture browses the web, reads pages, and returns insights—so you don't have to.

🎨 Real-World Example: Finding Invisalign Providers

Here's an example: Let's say you're selling to dental clinics and want to target Invisalign providers in Austin.

That data doesn't live in ZoomInfo or Clearbit. And dentists' offices aren't all on LinkedIn. But Claygent can run a real-time search and return those exact matches—saving you hours of manual work.

Here's how you can run this play:

  1. Start with Google Maps. Use a Google Maps enrichment to search for dentists within Austin. Set filters like radius, number of reviews, or star rating to build a quality base list.
  2. Use Claygent to filter for Invisalign. Next, add a column to your Clay table and use Claygent to visit each dentist's website. Ask it to check whether they mention Invisalign on their site. For bonus points on speed - use the metaprompter to get to a better prompt faster.
  3. Filter and prioritize. Now that you have this last-mile insight, you can narrow your list. For example:
    • Only show dentists with 4.5+ star ratings
    • At least 100 reviews
    • And that explicitly mention Invisalign services

In just a few steps, you've gone from a generic list of dentists to a high-intent, hyper-targeted lead list.

🌟 Other Use Cases for Claygent

Claygent is your tool for "last mile" discovery. You can set up Claygent to fill in virtually any data gaps, like:

  • Summarizing a founder's blog post or podcast before reaching out
  • Finding recent quotes from your prospect in the news
  • Verifying whether your favorite local restaurants are still active
  • Identifying niche law firms that focus on something specific—like AI IP

💪 The Competitive Advantage

This is the kind of data no one else is automating. Claygent fuels what we call "GTM Alpha" at Clay. GTM Alpha is the custom edge that gives you an advantage over your competitors.

The ability to automatically gather highly specific, contextual information about your prospects means:

  1. Your outreach can be more personalized and relevant
  2. Your targeting can be more precise
  3. Your SDRs can spend more time selling and less time researching
  4. You can discover opportunities your competitors miss entirely

🌠 Conclusion

Claygent solves the last-mile data problem by providing custom, AI-powered research capabilities that go beyond what traditional data providers can offer. By automating the collection of niche, high-value data points, you can enrich your prospect profiles with information that truly matters to your sales process.

Next up, we'll cover yet another way to handle tricky research tasks where data isn't readily available from a provider.

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AI-Powered GTM Automation

Enrich with Perplexity

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AI Skills Certification

Showcase your proficiency in leveraging AI within Clay to transform raw data into intelligent, actionable insights. Demonstrate your ability to engineer effective AI prompts, enrich data in creative ways using Claygent and AI tools, intelligently classify complex information, create dynamic AI-powered content, handle edge cases with conditional logic, and strategically combine multiple AI capabilities to solve sophisticated problems.

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