Predictive analytics built from your own history with the accuracy reported
We build forecasting models from the data your business already records, for demand, sales, stock, or churn. We report how accurate each model is before it is used for planning.
A forecast is an estimate with a range, and the model reports that range alongside the figure.
Based in Lahore, we work with businesses across Pakistan and internationally.
Check the history before building the model
A forecast can only be as good as the history behind it. We start by reviewing the data you record: how far back it goes, how consistent it is, and which events, such as price changes or holidays, need to be marked. Where the history is too short or too broken to forecast, we say so before any model is built.
Models are tested on past periods they have not seen, and the accuracy from those tests is reported with the forecast. The team then decides how much weight to give it.
Quick answers
What can be forecast?
Anything with enough consistent history: demand by product, sales by month, stock needs, customer churn, or cash flow. We check the history before committing.
How accurate is it?
It depends on the data. We test each model on past periods it has not seen and report the error, so you know the range before relying on it.
How much history do we need?
Usually two or more years for seasonal patterns, and at least several months for shorter cycles. We confirm after reviewing your data.
What a forecasting project can include
Data review
Assess how much history you have, how consistent it is, and what needs to be marked.
Model building
Build and compare models for the measure you need to forecast.
Accuracy testing
Test on past periods the model has not seen and report the error.
Delivery
Deliver forecasts to a dashboard, a spreadsheet, or a system on a schedule.
Review
Retrain and re-test the model as new data arrives, with the accuracy tracked over time.
Standard forecasting methods, chosen by testing
There is no single best forecasting method. We compare several on your data and keep the one that tests best, from classical time-series methods to machine learning models where the data supports them.
- Methods: Classical time-series and machine learning models compared on your data
- Testing: Held-out past periods used to report accuracy
- Delivery: Dashboards, spreadsheets, or a system connection
- Retraining: Models updated on a schedule as new data arrives
Modeling
Data and features
Serving and safety
Compare two scenarios for time spent preparing forecasts
Enter your own figures to compare the hours your team spends preparing forecasts and plans now with a second scenario you choose. This shows the arithmetic difference between the two; it does not forecast what a model will achieve.
These figures compare only the assumptions you enter. They do not include the cost of the build, and they do not predict time savings.
Review the history
Assess the data you record, how far back it goes, and how consistent it is.
Define the forecast
Agree what to forecast, how far ahead, and how it will be used.
Build and compare
Build several models and test them on past periods.
Report the accuracy
Deliver the forecast with its tested error and range.
Deliver and retrain
Deliver on a schedule and update the model as new data arrives.
Where a forecast changes a decision
Retail and e-commerce
Demand by product to plan stock and promotions.
Distribution
Reorder points and quantities from demand and lead times.
Subscription businesses
Churn risk by customer to focus retention effort.
Finance teams
Cash and revenue forecasts with a range for planning.
Plan from a tested estimate instead of a guess
A forecast with a reported error is more useful than a confident guess, because the team knows how far to trust it. We report the accuracy with every forecast and track it over time, so a model is kept only while it tests well.
- Built from your own recorded history
- Accuracy reported from held-out tests
- Ranges shown alongside figures
- Retrained as new data arrives
Predictive analytics pricing
Predictive analytics and forecasting starts from Rs. 150,000. The final quote depends on the number of measures forecast, the state of the data, and the delivery and retraining in scope.
Predictive Analytics: your questions
How much historical data do we need?
How accurate will the forecast be?
Can it forecast a new product with no history?
How often is the model updated?
How much does predictive analytics cost?
Do you work with businesses outside Lahore?
Tell us what you need to forecast
Describe the measure you plan around and the data you record. We will tell you whether the history supports a forecast and what it would involve.