85% Faster Pre-call Research for Sales Team with AI-Lead Enrichment

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faster pre-call research per lead

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less manual data entry from core enrichment fields

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Improvement in CRM data consistency for enriched lead records

The Bottom Line

This project supported a mid-sized professional services firm that wanted to reduce the time its team spent researching prospect websites before discovery calls. Reps were manually looking up details like company address, time zone, and basic business context, then entering that information into Airtable. The work was repetitive, inconsistent, and hard to scale.

ProsperSpark built an automated enrichment workflow using Airtable, Zapier, and ChatGPT. When a new lead was added, the automation pulled website content, identified useful business details, generated a short company summary, and wrote the validated results back into Airtable. The client ended up with a faster, more repeatable way to prepare for sales conversations while improving data quality and reducing manual effort.

Situation

Before the project, the client’s team was doing website research by hand as part of lead intake and discovery prep. That meant reps or internal staff had to visit company websites, hunt for basic information, interpret what they found, and manually enter the details into the CRM.

That process created a few problems at once. It took time away from higher-value sales work. It introduced inconsistency because different people might capture different levels of detail. And it left reps less prepared than they could have been going into early conversations.

The business did not need more raw lead data. It needed better lead context earlier in the process, without adding more repetitive admin work.

ProsperSpark used AI and automation to turn website research into structured CRM data, giving the client a faster and more scalable pre-sales process.

Solution

ProsperSpark built an automated enrichment workflow using Airtable, Zapier, and custom AI automation to enrich lead data before discovery calls.

From there, the workflow:

  • Parsed website text from the lead’s company site
  • Identified the business address from available site content
  • Used ChatGPT to infer the likely time zone from that address
  • Generated a short company description for quick rep context
  • Validated outputs before writing them back into Airtable
  • Added error handling for missing, invalid, or unusable websites

The team also reviewed common website structures, mapped the output fields to the client’s CRM process, and iterated through testing to improve reliability. That mattered because website content is often messy, inconsistent, or incomplete. The workflow needed to be useful in the real world, not just in ideal examples.

Results

The finished solution gave the client a more scalable way to turn public website information into structured CRM data.

Instead of spending time manually researching and typing in the same types of details over and over, the team could rely on automation to handle much of that work upstream. Reps had better context before discovery calls, and the CRM held more usable information earlier in the sales process.

The intake notes also indicate the client postponed hiring a full-time employee because of the improved workflow. While the exact savings were not provided, that points to meaningful operational value beyond just time reduction. Just as important, the process became more repeatable and easier to scale as lead volume grew.

Internal notes also suggest the relationship continued into later proposal activity, which supports the idea that the project delivered enough value to build confidence for additional work.

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At a Glance

Client
Professional services form

Organization

  • Mid-sized firm using Airtable to support lead intake and discovery preparation 

Business Challange

  • Reduce the manual time spent researching company websites and entering basic lead context into Airtable before discovery calls

Market Considerations

  • Professional services firms often rely on fast, informed early sales conversations, which makes good lead context valuable.
  • Public company information is usually available, but collecting it manually does not scale well.
  • CRM enrichment workflows need validation and exception handling because website content is inconsistent by nature.

Key Takeaways

  • AI can be useful in sales operations when it is paired with structured workflow design and validation.
  • Automating small but repeated research tasks can create meaningful capacity gains.
  • Better upstream data improves more than efficiency. It also improves readiness, consistency, and scalability.

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