Clay AI promises faster research, scalable enrichment, and AI-driven insights for outbound prospecting. At Hireframe, we tested Clay on two large projects to see how it performs in practice: what it speeds up, where it falls short, and how our team bridges the gap between raw AI output and reliable sales data.

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What We Learned

Our work with Clay confirmed several important realities about using AI for prospecting and enrichment:

AI accelerates research but can’t replace human oversight.

Clay compressed days of research into hours, but every dataset required a final pass. Human review remained essential to correct errors, validate contacts, and fill gaps before anything was ready for outreach.

Prompt quality sets the ceiling on output quality.

Effective prompts and repeated refinements determined whether the results were actionable. Each project required multiple iterations to tighten accuracy and relevance. A key part of this was referencing the correct variables from the imported dataset—for example, making sure prompts for look-alike companies pointed to the exact company field in the list. When variables were missing or unclear, Clay either errored or pulled information from unintended sources.

Trained operators give AI tools their real impact.

AI can generate data quickly, but turning that raw output into something a sales team can rely on still depends on people. At Hireframe, our Account Development Reps (ADRs) learn to design precise prompts, identify false positives, and validate results—skills that ensure AI-generated data is accurate and ready to use. This disciplined review is what makes tools like Clay practical and effective for outbound prospecting.

These lessons guide how we deploy AI internally and how we prepare our ADRs to work with emerging tools—ensuring that automation supports scale without sacrificing accuracy.

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Project 1: Enriching 2,300 Properties in Under 3 Hours

Starting with a list of property names and addresses, we needed to verify addresses and websites, add General Manager names with LinkedIn profiles and contact details, and capture basic amenities information.

Using Clay’s waterfall search, we confirmed websites and addresses, then applied Use AI to enrich each record with manager details, LinkedIn links, contact information, and amenity notes. 

To improve match rates and cut false positives, we refined prompts multiple times—making sure each prompt correctly referenced the property variables in the imported dataset (such as property name). Omitting these variables risked errors or data being pulled from unintended sources.

The results were strong. Clay enriched roughly 2,300 properties in under three hours. When properties were clearly identified, it reliably matched the correct General Manager on LinkedIn.

However, vague or duplicate names produced false positives or outdated sources, and even with improved prompts, some websites required manual correction. One ADR reviewed every record, filling gaps and correcting errors to ensure the dataset was ready for use.

While the first AI runs were never final, prompt refinement and careful validation meant that the end-to-end process was still far faster than a manual build.

Project 2: Turning 700 Leads into Look-Alike Companies and New Leads

From a list of 700+ leads, our goal was to confirm each lead’s current company, generate look-alike companies within our ideal customer profile (ICP), and then pull new contacts from those look-alike companies.

We used Use AI to verify company details, generate lookalike companies, and identify potential contacts. Throughout the process, we refined the prompts several times—ensuring each prompt explicitly referenced the correct company variable from the imported list. Without that anchor, Clay could error or pull data from unrelated sources, lowering accuracy.

This project also demonstrated clear gains in speed and scale. About 70 % of uploaded rows generated results, and roughly 90 % of those were usable after verification.

Common issues included suggestions of very large companies or roles outside our ICP. A single ADR reviewed and cleaned the entire list in three days, validating company fit and contact details before the data could be used for outreach.

Even with this human review, the Clay + ADR workflow was significantly faster than building a comparable list manually, proving its value when paired with disciplined oversight.

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Pros and Cons of Clay

Pros

• Fast processing of large datasets
  
• Flexible prompts for enrichment and lead generation
  
• Broad reach that surfaces data impractical to collect manually
  
• Time savings that let ADRs focus on higher-value outreach
  
  

Cons

• Variable accuracy with false positives and outdated data
  
• Prompt sensitivity requiring multiple iterations and careful use of dataset variables for dependable results
  
• Human review required before data can be trusted
  
• Context issues where fields like amenities or ICP criteria remain inconsistent
  
  

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Clay proved to be a powerful accelerator, cutting research time dramatically. But it also reinforced a core truth: human judgment is what makes AI output usable.

The strongest results came when prompts were carefully written to reference the right dataset variables and when trained ADRs applied disciplined review to every record.

At Hireframe, we train ADRs to combine tools like Clay with smart prompt design and rigorous data validation, ensuring that what AI produces is fast, accurate, and ready for real outbound work.

Interested in exploring how AI-driven enrichment can support your growth—while ensuring quality through human oversight? Let’s talk.

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