Custom machine learning models trained on your historical data — scoring leads, forecasting revenue, flagging churn risk — so your team makes forward-looking decisions instead of reading rearview mirrors.
ML models that score leads, forecast revenue, and flag churn before it happens.
SaaS teams optimizing lead routing and prioritization
Customer success teams targeting churn before it lands
Finance teams improving revenue and pipeline forecasts
Marketing teams running data-driven segmentation
ML models that score leads, forecast revenue, and flag churn before it happens.
Custom machine learning models trained on your historical data — scoring leads, forecasting revenue, flagging churn risk — so your team makes forward-looking decisions instead of reading rearview mirrors.
Custom machine learning models trained on your historical data — scoring leads, forecasting revenue, flagging churn risk — so your team makes forward-looking decisions instead of reading rearview mirrors.
Custom machine learning models trained on your historical data — scoring leads, forecasting revenue, flagging churn risk — so your team makes forward-looking decisions instead of reading rearview mirrors.
Sales focuses only on leads with real conversion likelihood
Churn risk surfaces in time to actually intervene
Forecasts ground budgeting and hiring in real signals
Models stay accurate via monitoring and scheduled retraining
Map your workflow, tools, success metrics, and constraints.
Document inputs, outputs, edge cases, and the system architecture.
Implementation wired into your real environment with monitoring.
Runbooks, documentation, and a clean transfer to your team.
It depends on the model. Lead scoring usually wants ~12 months of conversion outcomes. Churn models want ~18+ months. If data is thin, we start with simpler rule-based scoring and upgrade as data accumulates.
Most data is. Cleanup is part of the build — joining sources, normalizing fields, handling missing values, and documenting the schema. Cleaner data often delivers more value than the model itself.
Every deployed model has monitoring: input distribution shifts, prediction-to-outcome calibration, and accuracy decay are tracked. Retraining schedules kick in automatically, with manual review checkpoints.
Book a free consultation. We'll scope your workflow and decide if this is the right first build for your team.
