20 Prompts for Clay Data Enrichment Logic

Introduction (~400 words)

Imagine you need to find the LinkedIn profile of a company’s CEO—fast. Maybe you’re a salesperson preparing for a big pitch, or a recruiter sourcing candidates. You could spend hours scrolling through search results, or you could ask an AI agent to do it for you in seconds. That’s the power of data enrichment: turning raw information into actionable insights without the manual work.

But here’s the problem: AI agents aren’t mind readers. If you ask, “Find the CEO’s LinkedIn,” you might get a generic list of executives—or worse, irrelevant results. The difference between useful data and noise? A well-crafted prompt. That’s where Clay comes in. It’s a tool built to automate data enrichment, but its real magic lies in how you guide it. Think of it like giving directions to a GPS: the clearer your instructions, the faster you’ll reach your destination.

Why Data Enrichment Matters

Data enrichment isn’t just about collecting more information—it’s about finding the right information. For example:

• Sales teams use it to identify decision-makers before cold outreach.
• Marketers enrich lead lists to personalize campaigns.
• Researchers uncover hidden connections between companies or people.

Without enrichment, you’re working with incomplete data. And in a world where speed and accuracy decide who wins, that’s a risk most businesses can’t afford.

The Challenge: Finding High-Value Data Points

The internet is a goldmine of data, but digging for specific details is like searching for a needle in a haystack. Try Googling “CEO of [Company] LinkedIn” and you’ll get a mix of news articles, outdated profiles, and ads. Even if you find the right person, their profile might be locked behind a paywall or buried under similar names.

This is where AI agents shine—but only if you tell them exactly what to look for. A vague prompt like “Find the CEO’s contact info” might return a generic email address. A precise one like “Scrape the LinkedIn profile URL of the current CEO of [Company], verify it’s active, and extract their public email pattern (e.g., [email protected])” gets you actionable results.

How This Guide Helps

This article gives you 20 ready-to-use prompts for Clay’s data enrichment logic. Each one is designed to solve a real-world problem, whether you’re:

• Hunting for decision-maker emails.
• Validating company details before outreach.
• Enriching lead lists with social media profiles.

No fluff, no guesswork—just prompts you can copy, paste, and tweak for your needs. If you’re tired of wasting time on manual research, this guide is your shortcut to smarter, faster data enrichment. Let’s dive in.

Understanding Data Enrichment Logic

Data enrichment sounds like a fancy term, but it’s really just about making your data more useful. Imagine you have a list of company names. That’s raw data—useful, but not very actionable. Now, what if you could automatically add the CEO’s name, their LinkedIn profile, the company’s revenue, and the latest news about them? That’s enriched data. It turns a simple spreadsheet into a goldmine of insights.

Businesses use data enrichment for all kinds of things. Sales teams rely on it to find the right contacts for cold outreach. Marketers use it to personalize campaigns. Investors and researchers depend on it for due diligence. Even customer support teams enrich data to better understand who they’re talking to. The goal is always the same: take basic information and turn it into something you can actually use to make decisions.

Raw Data vs. Enriched Data: What’s the Difference?

Raw data is like a blank canvas. It’s just numbers, names, or basic details without context. For example, a list of email addresses is raw data. It tells you who you can contact, but not why you should contact them or how to approach them.

Enriched data, on the other hand, is like adding color to that canvas. It includes:

• Contact details (job titles, direct phone numbers, social media profiles)
• Company information (industry, size, revenue, growth trends)
• Behavioral data (website visits, content downloads, past interactions)
• Contextual data (recent news, funding rounds, leadership changes)

The difference is huge. Raw data might tell you that “Acme Corp” exists. Enriched data tells you that Acme Corp just raised $50 million, their CEO is active on LinkedIn, and they’re hiring aggressively in your target market. Which one would you rather have when planning your next sales pitch?

How AI Agents Make Data Enrichment Smarter

AI agents are like super-smart assistants that can fetch and validate data at scale. You give them a prompt—like “Find the LinkedIn profile of the CEO at [Company Name]“—and they go to work. They scan websites, databases, and public records to find the most accurate and up-to-date information. But here’s the catch: AI agents are only as good as the instructions you give them.

A vague prompt like “Find information about this company” will get you vague results. The AI might return outdated data, irrelevant details, or even false positives (like confusing two people with the same name). That’s why precision matters. A better prompt would be: “Find the LinkedIn profile of the current CEO at [Company Name], including their job title, tenure, and recent posts. Verify the information is from the last 6 months.”

Why Your Prompts Need to Be Specific

Think of AI prompts like GPS directions. If you tell your GPS to “go somewhere nice,” it won’t know where to take you. But if you say, “Take me to the nearest coffee shop with 4+ star reviews,” you’ll get exactly what you need. The same rule applies to data enrichment.

Here’s how specificity changes the game:

Vague Prompt	High-Precision Prompt	Result
”Find the CEO of this company"	"Find the LinkedIn profile of the current CEO at [Company Name], including their job title, start date, and a recent post about company strategy”	Accurate, actionable data
”Get company revenue"	"Find the latest annual revenue for [Company Name] from a verified source like Crunchbase or their SEC filing. Include growth percentage from the previous year”	Reliable financial insights
”Find contact info"	"Find the direct email and phone number for the Head of Marketing at [Company Name], verified within the last 3 months”	Valid, up-to-date contacts

The more specific your prompt, the less work you’ll have to do later. No one wants to sift through outdated LinkedIn profiles or incorrect phone numbers. A well-crafted prompt saves time, reduces errors, and gives you data you can actually trust.

Common Pitfalls (And How to Avoid Them)

Even with the best prompts, things can go wrong. Here are a few mistakes to watch out for:

1. Relying on outdated sources – Data changes fast. A CEO who left last month won’t help your sales pitch today. Always ask for recent information (e.g., “from the last 6 months”).
2. Ignoring false positives – AI might confuse two people with the same name. Always include unique identifiers like job titles or company names to avoid mix-ups.
3. Overlooking data validation – Just because the AI found something doesn’t mean it’s correct. Cross-check with multiple sources when possible.
4. Being too broad – “Find everything about this company” is a recipe for information overload. Focus on what you actually need.

The key is to treat AI agents like interns—give them clear, detailed instructions, and double-check their work. That way, you’ll get enriched data that’s not just fast, but also accurate and actionable.

The 20 Essential Prompts for Clay Data Enrichment

Data enrichment is like giving your sales and marketing teams a superpower. Instead of spending hours digging through LinkedIn, Crunchbase, or company websites, you can use AI to find the exact information you need in minutes. But here’s the catch: the quality of your results depends on how well you ask for them. A vague prompt gives you vague data. A precise prompt gives you gold.

These 20 prompts are designed to help you extract the most useful, accurate, and actionable data for your business. Whether you’re trying to find a CEO’s email, track a competitor’s latest move, or personalize your outreach, these prompts will save you time and frustration. Let’s break them down by category so you can start using them right away.

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Basic Contact Information: The Foundation of Outreach

Every sales or marketing campaign starts with contact details. But not all emails or LinkedIn profiles are easy to find. These prompts help you cut through the noise and get the exact information you need.

• “Find the CEO’s email address for [Company Name].” This is one of the most common requests, but it’s also one of the trickiest. Many companies use generic email formats (like [email protected]), but others don’t. A good AI agent will check multiple sources—like company websites, email verification tools, or even past email campaigns—to confirm the format. Pro tip: Always verify the email with a tool like Hunter.io or NeverBounce before sending anything.

• “Retrieve the LinkedIn profile URL of the Head of Marketing at [Company].” LinkedIn is a goldmine for professional data, but finding the right person can be like searching for a needle in a haystack. This prompt helps you zero in on the exact role you need. Once you have the profile, you can check their recent posts, job history, or even mutual connections. Just remember: LinkedIn URLs can change if someone updates their profile, so double-check before using them.

Best Practices for Verification:

• Use tools like Clearbit or Apollo.io to cross-check email formats.
• For LinkedIn, look for the “blue checkmark” (if available) to confirm the profile is active.
• If the data seems outdated, try adding a time filter (e.g., “from the last 6 months”).

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Company-Specific Data: Digging Deeper

Want to know if a startup just raised funding? Or what jobs a company is hiring for? These prompts help you uncover the details that give you a competitive edge.

• “Extract the latest funding round details for [Startup Name] from Crunchbase.” Funding data is crucial for sales teams targeting startups. A company that just raised $50M is more likely to buy your product than one running on fumes. This prompt pulls the amount raised, investors, and even the date of the round. But don’t stop there—cross-reference with news articles or press releases to confirm the data is up-to-date.

• “Find the most recent job postings for [Company] on LinkedIn.” Job postings reveal a lot about a company’s priorities. If they’re hiring a “Director of Customer Success,” they might be struggling with retention. If they’re looking for a “Sales Development Rep,” they’re probably scaling. This prompt helps you spot trends before they become obvious. Bonus: Use the job descriptions to tailor your outreach (e.g., “I saw you’re hiring for X—here’s how we can help”).

How to Cross-Reference for Accuracy:

• Compare Crunchbase data with PitchBook or AngelList.
• Check the company’s blog or press page for announcements.
• Look for inconsistencies (e.g., a funding round listed on Crunchbase but not mentioned anywhere else).

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Competitive Intelligence: Know Your Rivals

Understanding your competitors isn’t about copying them—it’s about finding gaps you can fill. These prompts help you uncover their strengths, weaknesses, and blind spots.

• “Identify the top 3 competitors of [Company] and their key differentiators.” This prompt does two things: it lists competitors and highlights what makes them unique. For example, if you’re selling project management software, you might find that Competitor A focuses on “ease of use,” while Competitor B emphasizes “enterprise security.” Use this to position your product as the best of both worlds.

• “Pull the latest customer reviews for [Product] from G2 or Capterra.” Reviews are a treasure trove of insights. Customers will tell you exactly what they love (or hate) about a product. This prompt aggregates recent reviews so you can spot patterns. For example, if multiple users complain about “slow customer support,” you can highlight your 24/7 chat in your outreach.

Using Prompts to Uncover Market Gaps:

• Look for recurring complaints in reviews (e.g., “missing integrations”).
• Compare pricing pages to see where competitors are overcharging.
• Check Glassdoor for employee feedback—unhappy teams often mean poor customer service.

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Social Media and Digital Footprint: Beyond the Basics

Social media isn’t just for memes—it’s a goldmine for business intelligence. These prompts help you track executives, monitor brand sentiment, and even find content ideas.

• “Find the Twitter/X handle of [Executive Name] and their most recent posts.” Executives often share their thoughts on industry trends, company updates, or even personal opinions. This prompt helps you find their handle and analyze their recent activity. For example, if a CEO tweets about “AI adoption,” you can tailor your pitch around how your product leverages AI.

• “Locate the YouTube channel of [Company] and summarize their latest video content.” Video content reveals a lot about a company’s messaging and priorities. This prompt pulls the channel URL and summarizes recent videos. For example, if a SaaS company just posted a tutorial on “automating workflows,” you can use that as a conversation starter in your outreach.

Ethical Scraping Strategies:

• Always check a platform’s terms of service before scraping.
• Use tools like Phantombuster or Octoparse for ethical data collection.
• Avoid scraping private or sensitive information.

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Advanced Enrichment for Sales and Outreach

The best salespeople don’t just sell—they personalize. These prompts help you find the details that make your outreach stand out.

• “Generate a list of [Company]’s recent hires in the sales department.” New hires often mean new budgets, new pain points, or even new decision-makers. This prompt helps you identify who’s joining the team so you can reach out with a relevant message. For example, if a company just hired a “Sales Operations Manager,” you can pitch your CRM integration tool.

• “Find mutual connections between me and the CTO of [Company] on LinkedIn.” Warm introductions convert better than cold emails. This prompt identifies shared connections so you can ask for an intro. For example, if you and the CTO both know “Sarah from HubSpot,” you can say, “Sarah mentioned you’re looking for a solution like ours—can we chat?”

Personalization Techniques:

• Reference recent hires, funding rounds, or job postings in your outreach.
• Mention mutual connections to build trust.
• Use data from reviews or social media to tailor your pitch.

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Final Thoughts

These 20 prompts are just the starting point. The real magic happens when you tweak them for your specific needs. For example, if you’re targeting healthcare companies, you might add prompts like, “Find the latest FDA approvals for [Company].” Or if you’re in e-commerce, you could ask, “Pull the top-selling products for [Brand] on Amazon.”

The key is to experiment. Try different prompts, test the results, and refine your approach. Over time, you’ll develop a library of go-to prompts that save you hours of manual research. And the best part? You’ll have data that’s not just accurate, but actionable. Now go enrich some data—your sales team will thank you.

How to Optimize Prompts for Maximum Accuracy

Good prompts are like good recipes. If you just say “make dinner,” you might get anything from soup to cake. But if you say “make spaghetti with tomato sauce, garlic, and fresh basil—only use ingredients from my fridge,” you’ll get exactly what you want. The same rule applies to AI data enrichment. The more precise your prompt, the better the results.

So how do you write prompts that actually work? Let’s break it down.

The Anatomy of a High-Performing Prompt

A strong prompt has three key parts: specificity, context, and constraints. Miss one, and the AI might give you outdated data, wrong details, or irrelevant information.

1. Be specific – Don’t just ask for “company info.” Instead, say:

   • “Find the current CEO of [Company Name], including their LinkedIn profile, job title, and start date.”
   • “List the top 3 competitors of [Company] in the SaaS project management space, with their pricing models.”

2. Add context – Tell the AI why you need the data. For example:

   • “I need this for a sales pitch, so focus on recent news and funding rounds.”
   • “This is for a competitor analysis, so highlight their strengths and weaknesses.”

3. Set constraints – Limit the AI’s search to avoid bad data. For example:

   • “Only use data from 2023 or later.”
   • “Ignore any sources older than 6 months.”
   • “If the information is unclear, say ‘Not enough data’ instead of guessing.”

Here’s how a weak prompt becomes strong:

❌ “Find info about Apple.” ✅ “Find Apple’s latest quarterly revenue (Q2 2024), compare it to last year, and explain the biggest growth drivers. Only use official reports or trusted financial news from the last 3 months.”

See the difference? The second prompt tells the AI exactly what to look for—and what to avoid.

Avoiding Common Mistakes in Prompt Design

Even small mistakes can lead to bad data. Here are the biggest pitfalls to watch for:

1. Being too broad

• ❌ “Find the CEO’s email.”
• ✅ “Find the work email of [First Name] [Last Name], current CEO of [Company]. Verify it’s active by checking recent LinkedIn activity or company announcements.”

2. Ignoring conflicting data Sometimes, different sources say different things. A good prompt tells the AI how to handle this:

• “If multiple sources disagree, list all versions with their sources and confidence level (high/medium/low).”

3. Forgetting to validate AI can make mistakes. Always ask for proof:

• “Include the source URL for every piece of data.”
• “If no reliable source is found, say ‘No verified data available.’”

Testing and Validating Enriched Data

Even the best prompt won’t work perfectly every time. That’s why you need to test and verify the results.

How to check AI-generated data:

• Manual spot-checks – Pick a few results and verify them yourself (e.g., search LinkedIn or company websites).
• Third-party tools – Use APIs like Clearbit, Hunter.io, or Apollo to cross-check emails and contact details.
• Team feedback – If you’re in sales or marketing, ask your team: “Does this data look right?”

Case study: How a sales team cut bounce rates by 30% A B2B software company was sending cold emails—but 40% bounced because of bad data. They fixed it by:

1. Refining prompts – Instead of “Find the CEO’s email,” they used: “Find the work email of [First Name] [Last Name], [Job Title] at [Company]. Verify it’s active by checking recent LinkedIn posts or company news. If unsure, mark as ‘unverified.’”
2. Adding validation – They used Hunter.io to confirm emails before sending.
3. Testing in batches – They ran small tests before scaling up.

The result? Bounce rates dropped to 10%, and reply rates went up by 25%. The lesson? Good prompts + validation = better data.

Final Tip: Iterate Like a Pro

No prompt is perfect on the first try. The best way to improve? Test, tweak, repeat.

• Start with a basic prompt.
• Check the results—are they accurate? Useful?
• Refine based on what’s missing.
• Test again.

For example, if your first prompt returns outdated data, add: “Only use sources from the last 6 months.” If the AI gives too much info, say: “Keep the answer under 100 words.”

The more you practice, the better your prompts will get. And soon, you’ll have a library of go-to prompts that save you hours of manual research.

Ready to try it? Pick one of the prompts from this guide, run it through your AI tool, and see what happens. Then tweak it until you get the perfect result. Your data—and your team—will thank you.

Real-World Applications of Clay Data Enrichment

Data enrichment isn’t just a fancy term—it’s how smart businesses make decisions faster. Imagine trying to find a CEO’s email or track a competitor’s new product launch. Without the right data, you’re guessing. With Clay, you’re not just collecting information; you’re turning raw data into actionable insights. Let’s look at how real companies use these prompts to save time, close deals, and stay ahead.

Case Study 1: How a SaaS Company Built a Targeted Prospect List

A small SaaS company selling HR software needed more leads. Their sales team spent hours manually searching for decision-makers—CEOs, HR directors, and hiring managers. It was slow, and the data was often outdated.

They turned to Clay and used prompts like:

• “Find the LinkedIn profiles of HR directors at companies with 50-500 employees in the tech industry.”
• “Extract their work emails and verify if they’re active by checking recent LinkedIn posts or company updates.”

The results? Their conversion rate jumped by 30%. Instead of cold-calling random contacts, they reached out to people who actually needed their product. The team saved 15 hours per week—time they could now spend on closing deals instead of digging for data.

“Before Clay, we were shooting in the dark. Now, we know exactly who to talk to and why they’d care about our product.” – Sales Manager at the SaaS company

Case Study 2: Competitive Research Made Easy

A marketing team at an e-commerce brand wanted to track competitor product launches. They needed to know:

• When competitors released new products
• What features they highlighted
• How customers reacted

They combined Clay with tools like SEMrush and Ahrefs. Here’s how they did it:

1. Used Clay to scrape competitor websites for product pages.
2. Set up prompts like “Find all new product announcements from [Competitor] in the last 3 months.”
3. Cross-checked the data with SEMrush to see if the launches drove traffic spikes.
4. Analyzed customer reviews on Trustpilot and Reddit to spot common complaints.

With this process, they identified a gap in the market: competitors were ignoring a key customer pain point. The team adjusted their product messaging to highlight this missing feature—and saw a 20% increase in engagement on their next campaign.

Case Study 3: Due Diligence for Investors

Investors need to move fast, but bad data can lead to costly mistakes. One venture capital firm used Clay to evaluate startups before investing. They focused on two key areas:

• Financial health: Revenue growth, funding rounds, and burn rate.
• Leadership team: Backgrounds of founders and key executives.

They used prompts like:

• “Find the last 3 funding rounds for [Startup], including investors and amounts raised.”
• “Extract the LinkedIn profiles of the CEO and CTO, including their past roles and education.”

This helped them spot red flags early. In one case, they discovered a startup’s CEO had a history of failed ventures. In another, they found that a company’s claimed revenue growth didn’t match its public filings. These insights saved them from bad investments—and helped them double down on promising opportunities.

Why These Examples Matter

These case studies show that data enrichment isn’t just about collecting information—it’s about using it to make smarter decisions. Whether you’re in sales, marketing, or investing, the right prompts can:

• Save time by automating manual research.
• Improve accuracy by verifying data from multiple sources.
• Uncover opportunities that competitors miss.

The best part? You don’t need to be a data scientist to get started. With Clay, you can plug in a prompt, run it, and get actionable results in minutes. The key is to start small—pick one use case, test a few prompts, and refine as you go.

So, what’s your biggest data challenge? Maybe it’s finding the right leads, tracking competitors, or vetting potential investments. Whatever it is, there’s a prompt for that. Try one today and see how much faster you can work.

Advanced Techniques and Pro Tips

Data enrichment is powerful, but the real magic happens when you move beyond basic prompts. Think of it like cooking: anyone can follow a simple recipe, but chefs create masterpieces by combining techniques. The same goes for Clay data enrichment. Let’s explore how to take your prompts to the next level.

Combine Multiple Prompts for Complex Queries

Why settle for one data point when you can get three in a single request? Instead of running separate prompts for a CEO’s email, LinkedIn, and recent news mentions, bundle them together. For example:

*“Find the current CEO of [Company], including:

• Their verified work email (check for recent activity)
• Their LinkedIn profile with job title and tenure
• Any news articles mentioning them in the last 3 months”*

This approach saves time and ensures consistency. The AI processes the request as a single task, reducing errors that might occur when running separate queries. Pro tip: Use bullet points or numbered lists in your prompts to make instructions clearer for the AI.

Want to automate this further? Connect Clay with Zapier to create workflows. For instance:

1. Clay finds the CEO’s contact details
2. Zapier sends the data to your CRM (HubSpot, Salesforce)
3. A Slack notification alerts your sales team

This turns manual research into a hands-off process.

Use AI to Refine Prompts Dynamically

Sometimes, the first output isn’t perfect. That’s where dynamic prompt generation comes in. Let’s say your initial prompt returns too many results:

“Find the CEO’s email for companies in the SaaS industry.”

The AI might include startups with 10 employees. To narrow it down, ask GPT-4 to rewrite the prompt:

“Rewrite this prompt to exclude companies with fewer than 50 employees and prioritize those with recent funding rounds.”

Now, the AI focuses on more established companies. You can also use this technique to:

• Remove outdated data (e.g., “Ignore profiles not updated in the last year”)
• Add filters (e.g., “Only include companies in Europe”)
• Improve accuracy (e.g., “Verify emails by checking LinkedIn activity”)

This back-and-forth with AI turns vague requests into laser-focused queries.

Scale Data Enrichment Across Teams

Data enrichment isn’t just for solo users. Teams can work faster by sharing prompt libraries and best practices. Here’s how:

• Create a shared prompt repository: Store tested prompts in a Google Doc or Notion page. Label them by use case (e.g., “Lead Gen,” “Competitor Research”).
• Use version control: Track changes to prompts, especially if multiple people edit them. Tools like GitHub or even a simple changelog work.
• Integrate with CRMs: Push enriched data directly to HubSpot or Salesforce. For example, Clay can auto-fill contact fields with verified emails and LinkedIn profiles.

A real-world example: A sales team used Clay to enrich 500 leads in a day. They saved hours by:

1. Running a bulk prompt to find LinkedIn profiles
2. Filtering for decision-makers (e.g., “VP of Sales” or “Director of Marketing”)
3. Syncing the data to Salesforce with one click

The result? More accurate leads and faster outreach.

Final Tip: Test and Iterate

Even the best prompts can be improved. Start with a basic request, then refine based on the results. For example:

First attempt: “Find the CEO’s email for [Company].”

After testing: “Find the work email of [First Name] [Last Name], current CEO of [Company]. Verify it’s active by checking LinkedIn or recent company announcements. Exclude personal emails (e.g., Gmail, Yahoo).”

Small tweaks like these make a big difference. Over time, you’ll build a library of prompts that work every time.

Ready to try these techniques? Pick one prompt, experiment with combinations, and see how much faster you can enrich data. Your team will notice the difference.

Conclusion: Mastering Data Enrichment with Clay

You’ve just seen 20 powerful prompts that can transform how you find and use data. But here’s the real question: What will you do with them? The best prompts are only as good as the hands that use them. Let’s break down what really matters—and how to make these work for you.

The Big Lessons from These Prompts

Some prompts stand out because they solve real problems fast. For example:

• “Find the CEO’s LinkedIn profile and verify it’s active” – Perfect for sales teams who need warm leads.
• “List the top 3 competitors of [Company] and their pricing models” – Great for market research before a big pitch.
• “Extract all recent job postings from [Company] to spot hiring trends” – Useful for investors or recruiters.

The key isn’t just asking for data—it’s asking the right way. Be specific. Add validation steps. And always, always test your prompts. A small tweak (like adding “in the last 6 months”) can turn messy data into gold.

Your Next Steps: A Simple Checklist

Ready to put this into action? Here’s how to start:

1. Pick one prompt – Don’t try all 20 at once. Start with the one that solves your biggest pain point.
2. Run it in Clay – See what comes back. Is it useful? Too broad? Too narrow?
3. Refine and repeat – Adjust the wording, add filters, or ask for verification. The best results come from iteration.
4. Save your winners – When a prompt works well, bookmark it. Build your own library over time.
5. Combine with other tools – Clay works even better when paired with SEMrush, LinkedIn, or even Google Sheets.

Need more help? Clay’s documentation is a great place to start. Or check out guides on AI prompt engineering—learning how to “talk” to AI will save you hours.

The Future of Data Enrichment

AI isn’t standing still. Soon, we’ll see:

• Real-time enrichment – Imagine getting live updates on company changes, not just static data.
• Multimodal data – Pulling insights from text, images, and even videos (like earnings call transcripts).
• Smarter validation – AI that doesn’t just find data but confirms it’s accurate before you use it.

The tools will keep getting better. But the real advantage? Using them before everyone else does. Start small, experiment often, and don’t wait for “perfect”—just start.

So, which prompt will you try first? The data’s out there. Now go get it.