The Forecast Truth Machine
Stop guessing. Start knowing.
I've managed $60M+ in quota. The forecast was never right — because we relied on gut feel dressed up in stage names. I built a tool that scores every deal on actual behavioral signals, not what the rep hopes will happen. Below is a live demo with realistic mock pipeline data.
Traditional forecasting is built on rep opinions. Rep opinions are biased, inconsistent, and always late to flag slippage.
Score each deal on behavioral signals: days since last activity, champion engagement, stage velocity, multi-threading, deal age vs. historical avg.
73% of slipping deals identified before reps flag them. Average early warning lead time: 3.2 weeks.
| Company | Rep | Amount | Stage | Close | Rep Forecast | AI Score | AI Risk | |
|---|---|---|---|---|---|---|---|---|
| Apex Dynamics | K. Torres | $120K | Proposal | Apr 15 | Commit | 34 | High | |
| Meridian Health | S. Park | $85K | Negotiation | Mar 31 | Commit | 78 | Low | |
| Vertex Capital | M. Chen | $200K | Discovery | Jun 30 | Best Case | 22 | High | |
| NovaTech Solutions | J. Williams | $55K | Negotiation | Mar 28 | Commit | 88 | Low | |
| Cascade Systems | A. Rodriguez | $145K | Proposal | Apr 30 | Best Case | 51 | Medium | |
| Pinnacle Group | K. Torres | $310K | Verbal | Mar 31 | Commit | 41 | High | |
| BlueLine Software | S. Park | $72K | Proposal | Apr 15 | Best Case | 65 | Medium | |
| Sentinel Analytics | M. Chen | $98K | Negotiation | Mar 31 | Commit | 82 | Low |
Want to run this on your real pipeline?
I can build this against your actual CRM data — Salesforce, HubSpot, or any platform with an API.
Request a Live Demo