Pipeline Intelligence
Forecasts you can actually believe
AI analyzes every deal in your pipeline—engagement patterns, stakeholder involvement, competitive signals, and historical patterns—to predict what will actually close. No more sandbagging. No more happy ears. Just accurate forecasts.
1
Reps lie (or self-deceive)
Your forecast is built on rep judgment. Reps are optimistic about their deals—that's why they're in sales. But optimism isn't accuracy. Your "commit" includes deals that will never close.
2
You don't know deal health until it's too late
A deal goes dark for 3 weeks. The champion stops responding. A competitor enters late. By the time you notice the warning signs, the deal is already lost. No early warning system.
3
Historical data sits unused
Your CRM has years of closed-won and closed-lost data. Patterns exist—deals with certain characteristics close, others don't. But nobody's analyzing it. Every forecast starts from scratch.
Lantern analyzes every deal against patterns from your historical data—engagement velocity, stakeholder coverage, competitive presence, timing patterns. Each deal gets a health score based on facts, not feelings.
Detect deals at risk before they're lost. Champion went quiet? Decision maker not engaged? Timeline slipping? Competitor mentioned? Get alerts when deals show warning signs.
Machine learning models trained on your historical close data predict which deals will actually close. Weighted pipeline based on real probability, not rep optimism.
See where deals stall, which stages leak, what differentiates wins from losses. Actionable insights to improve pipeline performance over time.
How actively is the prospect engaging? Email response times, meeting frequency, content downloads. Healthy deals have consistent engagement.
Is the full buying committee involved? Champions, decision makers, influencers, procurement. Deals with single-threaded relationships are at risk.
Is the deal progressing on expected timeline? Close date pushes are a leading indicator of loss. Deals that slip often keep slipping.
Is a competitor in the deal? Who? What's your win rate against them? Competitive deals need different attention than uncontested deals.
When was last activity? Who initiated? What type? Deals that go dark for 2+ weeks rarely recover without intervention.
Is MEDDIC/BANT complete? Deals missing key qualification data are more likely to be poorly qualified and stall.
Does this deal look like past winners or past losers? Pattern matching against your own data reveals hidden risk.
- Reps update stage based on gut feel
- Manager asks "is this going to close?"
- Rep says "yes" (always)
- Forecast = sum of rep optimism
- Leadership discounts by arbitrary percentage
- Deals slip or disappear
- Post-mortem reveals obvious warning signs
- Repeat next quarter
- Deals scored based on actual engagement
- Health assessed against historical patterns
- Risks surfaced automatically, early
- Forecast weighted by AI probability
- Leadership sees reality, not optimism
- At-risk deals identified before loss
- Continuous learning improves accuracy
- Forecasts you can commit to
Use cases
30%
reduction in sales cycle, 2x more strategic conversations
VP of Sales at an enterprise software company
40%
higher meeting-to-opportunity conversion
Sales Director at a mid-market company
25%
improvement in competitive win rate
CRO at a growth-stage company
Karen Walsh
VP of Sales
