Prospect Intelligence Engine
Preparing for a sales meeting involves more than reading a prospect's website. Understanding the business, its competitors, digital presence, and potential challenges usually means researching several sources and connecting the findings manually. For High Growth LLC, I built a Prospect Intelligence Engine to make this process more structured and repeatable. The system uses Claude Code to coordinate two specialized AI agents. One researches the company, its market, competitors, and publicly available stakeholder information. The other reviews selected website pages for SEO and conversion opportunities. A central director agent manages the process and checks the evidence before producing three reports: a meeting battlecard, a detailed prospect analysis, and a record of sources and missing information. The goal was simple: spend less time gathering information and give sales teams a clearer picture of the businesses they're approaching.
Prospect research was taking too much time without consistently delivering useful insights.
Before a sales meeting, the team needed to understand what a potential client does, how it positions itself, who its competitors are, and where there might be opportunities to help. Finding those answers meant checking company websites, LinkedIn profiles, social media accounts, and competitor pages individually. The information was available, but scattered. Research took time, important details could be overlooked, and the quality of preparation depended heavily on whoever handled it. There was also a reliability problem. Without a clear way to verify findings, assumptions could end up being used as facts during a client conversation. High Growth LLC needed a more consistent way to research prospects without turning every meeting into a lengthy preparation exercise.
The challenge wasn't collecting more data. It was knowing what to look for and what to trust.
A basic AI research prompt could summarize a company's website, but that wasn't enough to prepare for a meaningful sales conversation. The research needed to cover several areas, distinguish verified information from assumptions, and highlight findings that could actually matter in a meeting. I divided the work into two responsibilities: understanding the business and its competitive landscape, and reviewing its website for potential improvements. I also introduced evidence labels and quality checks to ensure that important research areas weren't skipped and unsupported claims weren't presented as facts. An earlier version focused too heavily on raw website data. I adjusted the workflow to prioritize the company, its market, competitors, and decision-makers before going deeper into technical website observations.
A coordinated AI research system built around accuracy, coverage, and practical sales insights.
Better research. Better-prepared sales conversations.
We build AI systems that take repetitive research off your team's plate and turn scattered information into insights you can actually use.
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