Cadence

Software that pulls together scattered RevOps data — CRM, warehouse, billing — and turns it into a single weekly revenue forecast that sales leaders actually trust. Cross-references CRM pipeline signals against product usage, billing events, and support activity to reconcile what reps claim against what accounts are actually doing. Forecasting fails because of incentive misalignment, not data gaps. Reps sandbag or inflate depending on what their comp plan rewards, and managers apply their own gut-feel corrections on top. Most tools try to fix the data problem, but the real unlock is building a system that makes the rep's self-reported data irrelevant by triangulating it against signals they can't game — product usage, billing events, support tickets. When the forecast doesn't depend on what the rep typed, the political noise drops out. Lena lived this exact problem as Head of RevOps at a Series C SaaS company — she rebuilt a forecasting system that increased accuracy from 60% to 85%+ using the same CRM-warehouse-billing triangulation approach. Her founding engineer Marcus Tran built the Snowflake pipelines and Salesforce integrations at that same company. The first design partners trusted them because they can sit down with any RevOps team and speak their language from day one, having built and run the exact system they're now productizing. Three things converged: (1) The modern data stack has matured — most Series B/C companies have a warehouse (Snowflake, BigQuery) with the signals Cadence needs sitting ready to use. (2) Two years of belt-tightening made forecast accuracy a board-level conversation; CFOs are now asking why the number missed, and sales leaders need something defensible. (3) AI tooling made it practical for a two-person team to build the reconciliation and anomaly-detection layer that would have taken a 10-person data engineering team three years ago.