GoHighLevel Revenue Forecaster
Pulls every active opportunity from your GoHighLevel (GHL) pipelines, AI-classifies stage health and close probability, produces a 30/60/90-day revenue forecast with confidence intervals — and tells you the 5 deals to push this week to hit quota.
How it works
1. Forecast bands
Three forecast cards (Conservative p25 / Likely p50 / Stretch p75) plus a 12-week chart with shaded confidence bands and your quota line.
2. AI weekly briefing
Claude streams a 200-word weekly briefing referencing your live forecast totals, naming the specific deals driving variance, and recommending where to focus.
3. Top 5 deals to push
AI-ranked list of the 5 highest-leverage deals to push this week, each with rationale (decision-maker engagement, days-in-stage, value). One-click ‘Draft outreach’ produces SMS + email + voice script and queues it via GHL workflow.
60-second Loom
60-second flow: dashboard loads with forecast bands ($48k / $74k / $92k vs $85k quota) → 12-week Recharts chart → Claude briefing streams referencing Acme + Northwind → click Draft on Acme → side panel shows context + SMS + email + voice script → Send via GHL workflow → confirmation + ‘Outreach scheduled’ badge.
Loom embed coming soon — until then, click through to the live demo.
Get the full build spec PDF
One-page PDF with the architecture, the tech stack, the timeline (2–3 weeks), and the investment range ($9,990 – $25,000). Sent to your inbox in under a minute.
Who it’s for
Sales leaders, founder-led GTM teams, and fractional CROs running GHL CRM who need a weekly ‘what to push’ answer instead of a static report — and want forecast bands trustworthy enough to commit to a board.
Tech under the hood
Frequently asked questions
How accurate is the forecast?
On a calibration set of 200 closed deals across 6 months, forecasts within ±15% of actual at p50 for 78% of weeks. Improves with rep-specific training.
Does it learn from our actual close patterns?
Yes — on each closed deal (won/lost), the model recalibrates close-probability mappings per rep + per pipeline. No PII leaves your tenant.
Can it factor in seasonal patterns?
Yes — pulls 12-month trailing data, detects seasonality (e.g. Q4 surge), and incorporates as a multiplier on forward forecasts.
What about the human override?
Every Push-this-week recommendation can be dismissed or snoozed; dismissed reasons feed back into the model so it learns your rep’s veto logic.
Want one of these in your business?
We’ll scope, build, and hand off a production-ready system in 3–4 weeks.