SD-301g · Module 1

AI Research Pipelines

3 min read

An AI research pipeline automates the intelligence gathering that a skilled rep does manually. Input: the prospect's name and company. Output: a structured research brief with company context, individual context, trigger events, and suggested personalization hooks. The pipeline scrapes LinkedIn, company websites, news sources, job boards, and public filings. It extracts relevant data points, discards noise, and produces a one-page brief in under sixty seconds. A rep reading this brief knows more about the prospect than 95% of the people who will contact them today.

The quality gate matters more than the speed. A research pipeline that produces inaccurate personalization is worse than no personalization at all. "I saw your company recently raised $50M" sent to a bootstrapped company destroys credibility instantly. Every AI-generated data point needs a confidence score. Data points below the confidence threshold are excluded. The rep reviews the brief before it is sent — not every word, but the key claims. The pipeline produces the intelligence. The rep validates the quality. The division of labor is clear: AI does the research, human does the judgment.

Do This

  • Assign confidence scores to every AI-generated data point and filter below threshold
  • Have reps review the top personalization hook before sending — not the whole brief, just the claim that matters
  • Test the pipeline on known contacts first to validate accuracy before scaling

Avoid This

  • Send AI-generated personalization without any human review — one wrong fact destroys ten right ones
  • Use the same research sources for every prospect — industry-specific sources produce better intelligence
  • Prioritize volume over accuracy — ten accurate, personalized messages outperform one hundred generic ones