Building an AI-Powered Sales Intelligence Pipeline from Scratch

Sales teams spend 40% of their time on research before making a single call. Finding company details, identifying decision-makers, understanding tech stacks, tracking funding rounds, analyzing competitive positioning. It's manual, repetitive, and expensive. I recently built an AI research pipeline that automates most of this. Here's the architecture and the lessons from making it production-ready. The Problem A typical sales researcher needs to answer questions like: What does this company actually do? Who are the decision-makers? What tech stack are they using? Have they raised funding recently? What are their pain points based on job postings and reviews? How do they compare to competitors? Doing this manually for 50 prospects takes a full day. An AI pipeline does it in minutes, with better coverage and consistency. Architecture Overview Prospect List (URLs/names) | v [Web Scraper] --> Company website, LinkedIn, Crunchbase, job boards | v [Content Store] --> Raw pages stored with met