A reimagined funnel platform built for demand and pipeline ops. Stages that respond to live signals, conversion lifters per stage, side-step paths for revivable leads, and AI fixes that explain themselves.
If your CRM funnel still looks like a checklist of stage names, you're not running a funnel. You're running a spreadsheet with stages on top.
Aware, Engaged, Considering, Decision. Each stage watches its own signals (intent score, page visits, reply heat) and reshapes the next-best action without a manual rule.
When a lead stalls, the funnel forks. Side-step paths route them into a recovery branch with a different lifter, a different channel, and a different angle. No dead-end "lost" bucket.
The platform reads stage friction (drop-off, time-in-stage, segment skew) and proposes the smallest change that moves it. Each fix arrives with a one-line reason and a preview.
Two panels run the day: the live stage board on the left, the AI fix queue on the right. No tab juggling, no exports.
Funnel Nova is shipped as three connected modules. You can turn any of them on or off without breaking the stage logic.
The core. Four signal-aware stages plus side-step branches, driven by the live data your CRM already produces.
Twelve packaged plays mapped per stage. Each lifter ships with a baseline, a variant, and a clean before/after read.
Reads stage friction and proposes the smallest change that moves it. Every suggestion arrives with a reason and a preview.
Two anonymized engagements. Codenames protect the buyers, the metrics are real.
A mid-market data platform was bleeding leads at Considering. The friction read showed a duplicate UTM source tag inflating attribution. After a hygiene fix and a swapped nurture subject, side-step paths started catching stalled leads instead of dropping them.
A devtools company was running broad A/B tests with messy reads. They switched to stage-mapped lifters. Engaged got a subject swap, Considering got a CTA swap, Decision got a channel swap. Each lifter had a clean before/after window.
Send a note. We'll walk you through the stage board, the lifter library, and how AI fixes show up in the queue, using a sample workspace seeded with realistic data.
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