Step 01
Upload your data
Share historical win/loss data enriched with firmographic data (for example via Vainu or Profinder) so the model can learn. Then upload your current prospect CSV and get ranked leads in minutes.
Swobuzo
Close more of the right deals. Spend less time on the wrong leads.
Swobuzo uses machine learning to rank every lead by conversion likelihood, so your team focuses where revenue is most likely to happen.
Lead Queue
Model liveNorthbeam Logistics
Predicted close rate
OptiScale Systems
Predicted close rate
Cranewell Group
Predicted close rate
The problem
Your sales reps are working hard, but not always on the right leads.
Most teams treat every inbound lead the same. That means low-fit accounts get time, while high-value prospects can sit untouched.
Time gets burned on low-quality opportunities.
Lead routing ignores true buying intent.
High-value prospects are missed or delayed.
How it works
Three steps from raw data to smarter pipeline focus.
Step 02
Learn from wins and losses
The model finds patterns in historical outcomes to detect what predicts a closed deal.
Step 03
Act on clear priorities
Get ranked leads and ideal customer profile signals your team can use immediately.
Product demo
A UI built for fast decisions.
See exactly where your next wins are.
Start with a clean upload flow for CRM or CSV data. Swobuzo then scores each record based on patterns learned from historical wins and losses.
The output is direct: a prioritized list with clear score bands and profile cues, so reps know what to work first and why.
Outcomes
The result is measurable sales impact.
Higher conversion rates
Better use of sales time
Data-driven decisions
Faster pipeline velocity
Proof
Trusted by teams that care about pipeline quality.
The clients we've had the pleasure of working with.
Ready to prioritize the leads that actually close?
Book a demo and see what your pipeline looks like when every rep starts with the highest-value opportunities.