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AI Lead Research.

You cannot personalize outbound without data. How AI SDRs research companies, validate contacts, and normalize context before drafting.

Data Precedes Generation

The biggest mistake teams make when deploying AI in sales is focusing entirely on the prompt. If you give an LLM a weak input (just a name and a company), it can only generate weak output (a generic sales pitch).

To generate hyper-personalized copy, the AI must first act as a researcher. This phase is known as Waterfall Enrichment.

The Waterfall Method

No single B2B data provider has perfect information. Cognlay's AI SDR architecture queries multiple data vendors in a sequence:

  1. It asks Provider A for the prospect's tech stack.
  2. If Provider A fails, it automatically queries Provider B.
  3. It scrapes the company's recent news, blog posts, and open job listings.
  4. It normalizes all of this unstructured data into a clean JSON profile.

Once this "research dossier" is complete, it is passed to the generation engine. Because the engine now knows that the prospect's company just posted three open roles for Salesforce Admins, it can draft a highly relevant email about CRM optimization.

Ready to master personalization?

Read our complete guide to scaling this workflow across your entire pipeline.

Read the Hyper-Personalization Guide