We offer both DISC and OCEAN (Big 5): across 36 derived behavioral dimensions (e.g., influence, detail orientation, risk appetite). Where relevant, we show which sub-dimensions influenced a recommendation.

Agent Pi focuses on building a full profile of every buyer, which includes not just their personality, but also their media appearances, hobbies, interests, skills and more. While Crystal indexes majorly on personality alone, Agent Pi is built with the belief that sellers need to deeply understand their prospects holistically and not only their personality. Therefore, messaging and playbooks are produced leveraging both behavioral and other signals. It's also worth mentioning that Agent Pi's personality prediction algorithm is often said to be more precise, as noted by many users, including Andy Whaley, Chief Growth Officer at JMARK , Michael Norton, EVP Enterprise at Sandler and many more Buyer First Sellers. People insights from Agent Pi are often used in combination with Account insights from Agent Miia, which often turns 2 + 2 into 8.

A Buying Committee Map identifies champions, influencers, decision-makers, and blockers within a deal and categorizes them into roles like Crusaders, Friendlies, Skeptics, and Neutrals. It is highly valuable for any enterprise deal with multiple stakeholders as salespeople's interactions are often limited to 3-5 top contacts while there are multiple stakeholders impacting the deal behind the scenes.

Each profile includes a confidence score and source list. If an insight is flagged wrong, you can submit feedback from the UI; corrections feed back into our validation pipeline and customer-specific models where applicable.