Software · Data and AI
Predictive analytics
Forecasts and risk scores built from your own history and shown inside the tools where decisions are made.
Most businesses already hold the data to see what is coming: past orders, payments, sensor readings, renewals. We build forecasting and scoring models from that history, test them on periods the model never saw, and put the results inside the tools your teams use. Each score comes with the main reasons behind it, so people can judge when to trust it.
What we build
- Demand forecasts by product, store and week that feed purchasing and production plans
- Churn and renewal risk scores for subscription and contract customers
- Late-payment predictions that help finance plan collections before invoices age
- Predictive maintenance alerts from machine sensor data and service history
- Lead scoring that ranks enquiries by likelihood to convert for the sales team
- Monitoring that compares predictions with outcomes and flags when a model drifts
DemoInteractive · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
| Account | Annual value | Risk score | Risk | Renews |
|---|---|---|---|---|
| Northwind Traders | ₹4,80,000 | 18 | Low | 2027-01-14 |
| Harbor Clinics | ₹2,60,000 | 71 | High | 2026-12-02 |
| Kestrel Logistics | ₹9,10,000 | 44 | Medium | 2027-02-20 |
| Sunfield Schools | ₹1,50,000 | 82 | High | 2026-11-25 |
What it does well
Back-tested models
Models are scored on past periods they never saw, against the simple method you use today.
Reasons shown
Each prediction lists the factors that moved it, such as falling usage or late payments.
Ranges, not points
Forecasts include a likely range, so planners see the uncertainty instead of false precision.
Inside your tools
Scores appear in your CRM, ERP or dashboards rather than in a report nobody opens.
Drift alerts
When real outcomes start to differ from predictions, the team is told and retraining is planned.
Checked retraining
Models retrain on fresh data on a schedule, and a new model goes live only if it beats the old.
Questions
How much data do we need?
For weekly demand forecasts, two or more years of sales history helps the model see seasonality; for churn, you need enough past customers who left to learn from. We check your data first and tell you if a simpler rule-based approach would serve you better.
How accurate will the forecasts be?
We cannot promise a number before seeing your data. We measure accuracy on past periods held back from training, compare it with the method you use today, and only recommend rolling out a model that does better on your own history.
What if the model is wrong?
It will be wrong sometimes, which is why each score shows its reasons and a range. People make the decision; the model informs it. We track predictions against outcomes and alert you when accuracy slips so the model can be retrained or retired.