From data to decisions operations can act on.
Forecasting, scoring, anomaly detection, and analytics, all built into the systems where decisions actually get made.
Who needs this
Signals operations are running blind.
These symptoms are usually a sign that your data exists, but the path from data to decision is broken.
- Teams make high-stakes decisions from gut feel because the data is too slow.
- Forecasts are spreadsheet exercises updated once a month.
- Anomalies are caught by customers before they are caught internally.
- Business operators rely on engineering for every ad-hoc report.
What we build
Decision systems that get used.
Forecasting
Demand, revenue, capacity, and resource forecasts that update continuously and are trustworthy enough to drive planning decisions.
Scoring & prioritization
Lead scoring, churn scoring, risk scoring — models that help teams focus time on the right accounts, tickets, or cases.
Anomaly detection
Automated detection and alerting for operational outliers — before they become incidents, customer complaints, or audit findings.
Executive dashboards
Clear, trustworthy dashboards tuned to the decisions leadership actually makes — not walls of metrics nobody reads.
How we think
Why our models land in production.
Models integrated with operations
Predictions feed directly into the systems where decisions are made — CRMs, ticketing, operations consoles — not a standalone dashboard nobody looks at.
Measured on outcomes
We track business outcomes, not just model accuracy. A model that is technically accurate but operationally ignored is a failed project.
Ongoing evaluation
Monitoring, drift detection, and retraining pipelines so models keep working after launch — not just on the day they ship.
Related
Solutions behind this use case.
Have a decision worth automating?
Describe the decision. We'll tell you whether a model, a dashboard, or something else is the right tool.