A solar call center dials the leads a model says will convert
A propensity model trained on past customers sorts leads green, yellow and red so setters stop dialing the bottom deciles first.
1 sourced write-up.
A propensity model trained on past customers sorts leads green, yellow and red so setters stop dialing the bottom deciles first.
These general AI tools apply anywhere. They are not solar case studies. Showing sourced use cases that name ChatGPT, Claude (Projects), Claude, Claude Code, Perplexity API, and Perplexity.
Account research that took 15 to 30 minutes now takes about 2 to 3, using one structured ChatGPT prompt.
Reps warm up on an AI prospect that hangs up when they stumble, then get two strengths and two fixes.
Research on the prospect plus your own company goes in one Claude Project; out come cited, non-generic questions.
An enrichment connector inside Claude finds more people in the buying department so one champion becomes four.
Give Claude everything you know about the buyer and tell it to push back like they would.
Ops exposes Clay signals and a contact waterfall through MCP so reps get a ranked account list and verified contacts in chat.
Huge catalog, slow lead analysis.
Prep time limited customer time.
Team tripled; knowledge couldn't keep up.
100+ accounts per BDR.
90-minute manual research.
Territory plans took 10-12 hours.