Machine learning
Predictive analytics and machine learning
We turn historical data into forecasts, risk scores and early warnings, test them against past periods before anyone relies on them, and deliver them into the tools where decisions are made, with monitoring that shows when a model needs retraining.
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Predictions tied to a decision
- Python
- pandas
- scikit-learn
- XGBoost
- PyTorch
- Jupyter
- MLflow
- Apache Airflow
- Snowflake
- BigQuery
- PostgreSQL
- FastAPI
A prediction is only worth building if it changes what someone does: how much stock to order, which customers to call this week, which machine to service before it fails. Krapton builds predictive models around that decision, so the output arrives in the right system at the right time, with an explanation the people using it can check.
Most business prediction problems run on tabular and time-series data that already sits in a warehouse, CRM or ERP. For that kind of data, careful feature engineering with gradient-boosted trees or statistical forecasting often matches or beats more complex methods, and it is cheaper to run and easier to explain. We use deep learning when the volume of data and the shape of the problem justify it.
Every model is backtested against the method you rely on today, whether that is a spreadsheet, a fixed rule or a planner's judgment. If it cannot beat that on your own history, it does not ship, and we explain what would need to change first, such as a longer history, better labels or a different target.
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Predictions we build most often
The inputs are usually records you collect already, and every prediction ends in a specific action that someone takes.
Demand and sales forecasting
Forecasts by product, location and week, or revenue by segment and month, with prediction intervals that show the range of likely outcomes so planners can size safety stock and staffing.
Churn and retention
Identify subscribers or accounts likely to cancel, with the signals behind each score, so customer success can step in early and you can measure which interventions actually keep customers.
Lead and customer scoring
Rank leads by their likelihood to convert and customers by expected lifetime value, using CRM history and product usage, so sales and marketing effort goes where it is most likely to pay back.
Anomaly and fraud detection
Flag unusual transactions, orders, sensor readings or operational metrics as they arrive, tuned to your tolerance for false alarms and routed to the people who investigate them.
Predictive maintenance
Estimate failure risk or remaining useful life from sensor, telemetry and maintenance records, so repairs are scheduled before breakdowns and spare parts are ordered ahead of need.
Delivery and lead-time prediction
Predict delivery dates, supplier lead times and job durations from past orders, routes and workloads, so customer promises and production schedules reflect what is likely rather than what is hoped for.
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What each predictive project delivers
Data and target audit
A check of history length, label quality and data gaps for the outcome you want predicted, including whether a simpler method would serve you better.
Backtest report
Your current method, a simple baseline and the proposed model compared over the same historical periods, with errors broken down by segment.
Production model and feature pipeline
Versioned in a model registry, with features computed identically in training and production and data tests on every run.
Predictions where decisions happen
Scores and forecasts written to your warehouse, CRM, ERP or app through scheduled jobs or an API, with the main drivers attached to each prediction.
Monitoring dashboard
Input drift, prediction mix and realized accuracy as outcomes arrive, with alerts that go to a named owner.
Model card and runbook
What the model is for, where it is weak, who owns it, and how to retrain it, compare versions and roll back.
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How a model earns its place
A model has to beat the way you work today, on your own data, before it reaches a real decision.
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Frame the decision
We define the decision, the action it triggers, the forecast horizon and the cost of each kind of error. Those choices set the target, the evaluation metric and the threshold that turns a score into an action.
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Build point-in-time data
History is assembled at the right grain, with features that use only what was known at the moment of prediction. Leakage checks and data quality tests run on every build, because a leaked feature makes a weak model look far better than it is.
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Model and backtest
Simple baselines come first. Gradient-boosted trees, statistical time-series models, survival models or neural networks then have to beat them across several historical periods, not one lucky split, before they are considered.
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Deploy and monitor
Predictions are delivered in batch or through an API and tracked in a registry. Drift and realized accuracy are monitored, and a retrained challenger replaces the live model only after it wins a side-by-side comparison.
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Data, fairness and ownership
Most of the effort and cost in predictive work sits in data engineering: consistent definitions, clean joins and pipelines that keep running. Compute for tabular models is usually modest. Forecasts are ranges rather than promises, so we show the uncertainty alongside every number, and each model gets a named business owner who decides how its outputs are used.
When a score affects a person, as in lending, insurance, hiring or tenancy, the bar is higher. Rules such as the Equal Credit Opportunity Act in the US and the automated decision provisions of GDPR can require explanations and human review. We design for those duties with explainable features, bias testing across groups, reason codes and documented model governance, and your compliance team decides whether a use is appropriate.
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Related services
Business intelligence & analytics
Leverage data-driven insights with our BI and analytics tools.
Hire Scikit-learn developers
Hire Python developers
Supply chain software
We build TMS, WMS, control-tower and last-mile platforms that turn supply-chain spreadsheets into observable, automated operations.
Finance software
We build wealth-tech, treasury, capital-markets and CFO platforms where every decimal, every reconciliation and every audit trail matters.
AI for retail and e-commerce
Search, recommendations and service that lift conversion.
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Frequently asked questions
What is the difference between predictive analytics and business intelligence?
Business intelligence describes what has happened, through reports and dashboards. Predictive analytics estimates what is likely to happen next and scores individual cases, such as a customer's risk of leaving or next month's demand for a product. The two work best together, with predictions shown inside the dashboards and tools your team already uses.
How much historical data do we need?
Enough to cover the patterns you want the model to learn. Seasonal demand needs history that spans several seasons, and a churn model needs enough past cancellations to learn from. The data and target audit answers this for your case, and if the history is too thin, we say so and suggest what to collect first.
Which machine learning algorithms do you use?
Whichever wins the backtest at an acceptable cost and level of explainability. For tabular data that is often regularized regression or gradient-boosted trees such as XGBoost. Time series may call for statistical or global forecasting models, time-to-event questions for survival models, and large volumes of images, text or event sequences for deep learning.
How do you keep models accurate after launch?
By watching three things: the quality of incoming data, drift in inputs and predictions, and accuracy once real outcomes are known. Retraining runs on a schedule or when monitoring triggers it, and a new version replaces the live model only after it performs better on recent data in a side-by-side comparison.
Can predictions go straight into our CRM or ERP?
Yes. Batch scores can be written to your warehouse and synced to tools such as Salesforce, HubSpot or your ERP, and real-time scores can be served through an API at checkout, at sign-up or inside an internal app. Each prediction can carry its top drivers, so the person acting on it sees why.
Can you explain why a model made a prediction?
Yes. We report which factors matter across the whole model and, for each prediction, which ones pushed the score up or down, using methods such as SHAP. Where explanation matters more than a small gain in accuracy, for example in credit decisions, we recommend simpler, more interpretable models.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.