What is predictive ML?
Predictive ML is a machine learning model that guesses what happens next based on what happened before. Given your historical data. Customer behaviour, transactions, appointments, sales. The model learns patterns and produces a score or number for a new case. Will this customer churn? Will this lead convert? Will this appointment no-show? What will sales be next month? The model tells you before it happens.
How is predictive ML different from generative AI?
Generative AI writes text, images, or code. Predictive ML gives you a number or a probability. A generative AI drafts a newsletter. A predictive ML tells you which subscriber is 82% likely to unsubscribe next month. Different tools, different jobs. Predictive ML is older, cheaper to run, and usually gives you more explainable answers.
How much does predictive ML cost?
Prototype phase (data audit + baseline model + accuracy report) is ₹1.5-3 lakh. Full production build with feature store, retraining pipeline, monitoring, and integration into your app is ₹6-20 lakh. Ongoing operations start at ₹35,000/month and include monitoring, drift detection, and quarterly retraining. Feasibility call is free.
How much data do we need for a predictive model?
Depends on the problem. For churn or no-show scoring, we usually want 12+ months of historical data with at least 5,000-10,000 events. For demand forecasting, 2-3 years of daily or weekly data. For lead scoring, 1,000+ closed opportunities with outcomes. If you have less than this, we tell you before we take your money.
What accuracy can we expect?
Depends on the signal in your data. RealZent's ShowUpAI predicts site-visit no-shows with 84% AUC. Churn models we've built land in the 78-89% AUC range. Lead scoring typically hits 0.75-0.85 AUC. We'll tell you the baseline accuracy after the prototype phase, before you commit to production build.
What happens when the model gets stale?
That's called data drift. Behaviour changes. Economic cycle, seasonality, new product, new competitor. And the model starts predicting worse. We monitor drift automatically and trigger retraining. In the operations phase we retrain every 4-12 weeks depending on how fast your world moves. This is included in the monthly ops fee.
Which ML tools do you use?
For tabular data: XGBoost, LightGBM, CatBoost, scikit-learn. For time series: Prophet, NeuralProphet, PyTorch Forecasting. For deep learning: PyTorch or TensorFlow depending on team preference. MLflow for experiment tracking, Feast for feature stores, Kubeflow or plain Airflow for pipelines. Deployment: AWS SageMaker, Azure ML, or bare Docker if simpler.
Can predictive ML work without a data science team?
Yes. That's most of our clients. We handle the data science, MLOps, and monitoring. You just consume the score via an API or a dashboard. When we hand over, we give you an explainer doc so your team knows why the model says what it says. No black box.
