Built by a revenue leader who got burned by his own forecast.
2022. Miami. Jonathan Park had spent 6 years as a VP Sales at two B2B SaaS companies — managing 50–80 rep teams through enterprise deal cycles. Both companies missed Q4 by more than 15%. Not from bad reps. From a forecast process that only read what reps logged.
"I had a Salesforce dashboard full of deal stages that reps had clicked into. I had email threads full of signals nobody was reading. I built Valuevynt because the gap between those two things was costing me $2M a year in missed revenue attainment."
Jonathan spent six years as VP Sales at two B2B SaaS companies, managing enterprise deal cycles with 50–80 rep teams. Both companies ran quarterly pipeline reviews. Both used Salesforce. Both missed Q4 forecast by more than 15% — twice each. The post-mortems were always the same: the at-risk deals had been flagged as commit until the final weeks, when champions went quiet and close dates were pushed.
What nobody was reading was the behavioral data that had predicted those slips 3–4 weeks earlier. Email response latency had shifted. Thread depth had dropped. The champion who was CC'd on every thread had disappeared from the last four. The procurement contact appeared on the email chain and the rep didn't notice what that meant. All of that was sitting in metadata nobody had instrumented.
In 2022, Jonathan started building the tool with Marcus Chen as CTO. The design decision was simple: read signals from metadata only — timestamps, thread structure, participant lists, language patterns — never email body content. Score every open deal daily. Give the CRO a number they can defend, not a number that reflects rep optimism bias. That's the product.
Valuevynt is bootstrapped, based in Miami, and has been profitable since its first year. Neither Jonathan nor Marcus raised VC money. The product has to prove itself on forecast accuracy every quarter — and so far, it has.
Two people. One focused product.
Jonathan Park
6 years VP Sales at B2B SaaS companies managing enterprise deal cycles. Built the signal scoring framework directly from his own pipeline review post-mortems — the deals that slipped, the close dates that moved, the champions that went quiet. Based in Miami. Runs product direction, customer success, and every onboarding conversation personally.
Marcus Chen
Former ML engineer at a fintech company, where he built signal-extraction systems for transaction risk scoring. Built Valuevynt's 40-signal model and the metadata-only extraction pipeline. Based in Miami. Has never worked in sales — which turned out to be useful: no assumptions about what reps "should" log, just what the data actually contains.
What we believe about sales data — and what we don't do.
"Reps don't lie. They just know less than their emails do. CRM hygiene campaigns ask them to log more. We ask them to log nothing new."
"A commit number without a confidence score is rep optimism in a spreadsheet. A weighted forecast that accounts for deal slippage signals is something you can actually take to the board."
"We are not a call intelligence platform. We don't record sales calls, coach rep technique, or score conversations. Gong does that. We score deal health — the behavioral signals that exist outside of what was said on a call."
"Pipeline coverage ratio and quota coverage are lagging indicators. The leading signals are in your email metadata right now. Sales ops teams who spend their week on CRM hygiene campaigns are solving the wrong problem."
Talk to Jonathan directly.
No SDR sequence. No discovery call checklist. Just a conversation about your pipeline, your deal cycle, and whether signal scoring is the right fit for your team.
Email [email protected] →