Prove's problem wasn't a lack of data. It was that the systems holding it never spoke to each other. Deepline connected them, and gave the team a way to act on what showed up once they were.
Prove is a New York–based identity verification and fraud prevention company. Its revenue organization runs roughly 150 people across sales, SDR, post-sales, implementation, and RevOps.
Prove's revenue data lived in silos. Salesforce, its financial planning tool, its sales engagement platform, and its project tracker each held a piece of the picture, with no way to ask one question and get an answer that spanned all four. A full picture of the business meant logging into each system separately and stitching the answer together by hand. Reporting leaned heavily on spreadsheets, assembled the same manual way.
Account targeting was similarly blunt: a basic ideal-customer-profile model built on averages, average employee count, average revenue, with no way to weight the dozens of softer signals that actually predict a good-fit account.
The team had already tried to fix this. Andrew Kim, SVP of Revenue Operations, evaluated data-warehouse-centric analytics platforms before Deepline.
Every one of them required the same heavy lift: standing up a warehouse, building tables, defining KPIs from scratch. That work assumed a data engineering skill set his RevOps team didn’t have. "I'm a RevOps guy," Andrew said. "I'm not a data scientist."
Deepline connected the tools Prove already had, giving the team three practical ways to use its data.
Account-level enrichment gave Prove's RevOps team more than 20 signals beyond basic firmographics, including signals pulled from customer websites and signals outside Prove's own customer base, to score and surface lookalike accounts.
Where the team's targeting used to stop at averages, the field now works a scored, prioritized account list instead of a static ICP. That list is the direct output of connecting data Prove already had, not data it had to go acquire.
That same connective work made a second thing possible: a live executive reporting layer, pulling data from Salesforce, the financial tool, and outside sources into a single real-time view for the CRO, replacing the manually assembled spreadsheet reports he’d relied on before.
Andrew and others now bring that same connected data into daily ad hoc use, asking Deepline’s connected LLM questions as part of the regular workflow rather than waiting on a scheduled report. As Andrew put it, it functions "like a supercharged analyst — another body at the table providing extra intelligence."
Deepline and Prove built a pre-meeting agent: the day before a rep's first call with a prospect, a Slack message arrives packaging together LinkedIn activity, recent news, and job postings the rep would otherwise have spent time pulling together by hand.
Reps get back roughly 10 hours a week that used to go to that manual prep, time redirected to actual selling. Andrew estimates a similar 10 hours a week back for himself, from replacing ad hoc research and brainstorming with daily use of the connected LLM. Neither number required Prove to add a new system to the stack. Deepline sits alongside the tools Prove already had, connecting them rather than replacing them.
Bring together the prospect’s LinkedIn activity, recent news, and job postings.
Send the rep a Slack message the day before their first call with the prospect.
Reps start with the research assembled instead of pulling it together by hand.
“If it's a 40-hour week, 25% of your week is given back to you because of this.”
Andrew Kim
SVP of Revenue Operations, Prove
A revenue org that used to stitch its own data together by hand now works from a scored account list, a live executive report, and a daily research assistant, all pulled from systems Prove already owned. Reps get roughly 10 hours a week back on pre-call prep; Andrew estimates about a quarter of his own week back doing the same.
As Andrew put it: "It has changed the way we do everything... this would have never been on the radar a year ago."