Predictive Analytics Platform

Business intelligence tells you what happened. Predictive analytics tells you what will happen — and with what probability. BI generates reports and dashboards from historical data. Predictive analytics trains mathematical models on that same data, then applies those models to new inputs to generate forecasts, risk scores, and probability estimates. The output is not a chart. It is a number attached to a future event — a 73% probability this customer churns, a 12-day window before this motor fails, a 94% confidence that demand exceeds 5,000 units next month.

Three reasons, in order of frequency. First, data drift: production data diverges from training data within weeks, and without automated detection, accuracy degrades silently. Second, engineering debt: data scientists build models in notebooks; production requires containerized serving, monitoring, rollback capabilities, and latency guarantees that notebooks do not address. Third, organizational gaps: no clear ownership of model performance, no retraining schedules, no defined thresholds for when a model gets replaced. Platforms like DataRobot automate model training but often punt on the production hardening that determines whether a model survives month two.

Under 48 hours for pre-built use cases with standard data sources — demand forecasting, churn prediction, maintenance alerts. These ship with validated feature pipelines and model architectures. Custom models for novel prediction targets take 4-8 weeks from data assessment to production deployment. The bottleneck is rarely the model training. It is data quality validation, stakeholder alignment on prediction targets, and integration with downstream systems that consume the predictions.

Population Stability Index measures whether production input distributions have shifted from training baselines. Feature-level monitoring tracks each input variable independently — a single drifting feature can degrade the entire model. KL divergence and Kolmogorov-Smirnov tests quantify the shift statistically. When drift crosses configurable thresholds, automated retraining activates using recent data. The alternative — waiting until someone notices bad predictions — costs weeks of degraded accuracy and the business decisions made during that window.

Predictive Analytics generates the probability estimates. Decision Intelligence consumes them as inputs to scenario simulations and prescriptive recommendations. Example: the predictive model scores each customer's churn probability. Decision Intelligence evaluates retention interventions — discount offers, account manager outreach, feature upgrades — simulates their expected impact on each customer segment, and recommends the optimal action per account. Prediction without decision support is an expensive weather report. The two platforms together close the loop from forecast to action.

Your existing warehouse — Snowflake, BigQuery, Redshift, Databricks — stays where it is. The platform connects via native connectors, reads from your tables and views, and writes predictions back to your warehouse or serves them via API. No data migration required. Feature computation runs inside your warehouse using pushdown SQL or on dedicated compute depending on latency requirements. The only new infrastructure is the serving layer for real-time predictions.

Manufacturing: sensor time-series data from PLCs and IoT devices feeds maintenance prediction, quality defect forecasting, and yield optimization models. The data is high-frequency (sub-second readings) and high-volume (thousands of sensors per facility). Energy: load forecasting at 15-minute intervals drives generation scheduling and grid balancing. Renewable output prediction using weather data enables better capacity planning. Both industries share a pattern — high-volume sensor data, clear cost-of-error metrics, and regulatory requirements for model documentation. Industry AI deployments in these sectors use the same predictive infrastructure with domain-specific feature engineering.

Every prediction ships with SHAP values — the exact contribution of each input feature to that specific score. Not a global feature importance chart, but instance-level explanations. A credit score of 680 comes with "income-to-debt ratio contributed +45 points, payment history contributed +30 points, recent credit inquiries contributed -15 points." This satisfies OCC model risk management guidance (SR 11-7), GDPR Article 22 right to explanation, and FDA requirements for clinical decision support. Explainability is not a reporting feature bolted on after training. It is computed at inference time for every prediction.