Ramp reports faster go-to-market research with Perplexity Enterprise

Ramp says Perplexity Enterprise has changed how its go-to-market teams research customers, competitors and sales opportunities. In a Perplexity customer story and accompanying video published on September 8, the financial operations company describes using the tool across sales, product marketing, operations and customer experience. The story is a company-reported case study, so its productivity figures should be read as Ramp’s own assessment rather than an independent benchmark.

Before adopting Perplexity, Ramp employees describe spending hours moving between internal documentation, company websites, competitor pages and personal notes. Sales staff used that material to prepare for customer conversations, while product marketing checked claims for sales battlecards. Ramp says the process created manual handoffs and repeated fact-checking. With Perplexity, employees can ask questions in client-specific spaces, receive concise answers with clickable citations and continue with follow-up questions instead of rebuilding the research from scratch.

Perplexity’s customer page says Ramp adopted Enterprise across its go-to-market organisation in April 2025. It reports a 47% average productivity increase, multiple hours saved per employee each week and higher reported work quality. One sales employee says territory planning that previously took 10 to 12 hours can now be reduced to minutes. In the video, another Ramp employee estimates that preparation time for a customer call has fallen by about half and says the same time can support more output. These figures come from the companies involved and are not independently verified in the source.

The case also highlights a less visible part of enterprise AI adoption: connecting external research with internal workflows. Ramp says support employees found that Perplexity could combine its internal AI tools with documentation from integration partners’ help centres. The company points to enterprise controls and Perplexity’s stated policy of not training on enterprise data as factors in its security decision. Those claims describe the vendor’s offering; organisations still need their own access rules, retention settings and review procedures.

For AI users and makers, Ramp’s example suggests that the business value of an AI search tool may come less from a single dramatic answer than from removing small research delays across many teams. Cited outputs can shorten the path from question to action, but they do not eliminate checking. The practical lesson is to measure time saved, answer quality and error rates in a real workflow, while keeping a person responsible for decisions made from the research.