Predictive Analytics
Know what's coming before your competitors do.
Prediction without explanation is guesswork with better branding.
Demand forecasting, churn prediction, risk scoring, and predictive maintenance — systems that surface the signal before it becomes a problem. Every model ships with feature importance, confidence intervals, and clear documentation. Stakeholders understand what the model is doing and why.
Prediction meets action
A prediction without an action pathway is a report.
We design the prediction around the decision it enables, not around what's technically feasible. Predicting churn 30 days out is only valuable if there's a retention action that can be taken in that window. We build the downstream systems — alert triggers, CRM integrations, workflow automations — that turn predictions into interventions.
avg. prediction horizon enabling actionable intervention
AurvikAI predictive deployments
Decision-linked prediction
Every prediction target is defined by the action it enables and the decision window available.
Explainable outputs
Feature importance, confidence intervals, and model cards that stakeholders can understand without data science training.
Action pathways
Alert triggers, CRM automations, and workflow integrations that turn predictions into real business interventions.
Continuous monitoring
Drift detection, performance tracking, and automated retraining when business conditions change.
Reactive decision-making vs. predictive intelligence
The difference between responding to problems and preventing them.
Reactive operations
- Equipment failures discovered when they cause downtime
- Customer churn detected after the customer is gone
- Inventory shortages visible only when shelves are empty
- Demand spikes overwhelm staffing and capacity
- Risk events identified in post-mortem, not in advance
AurvikAI predictive systems
- Equipment failure predicted 72 hours before it occurs
- Churn risk scored 30 days out with retention actions triggered
- Inventory levels optimised against predicted demand curves
- Staffing and capacity aligned to forecasted demand
- Risk events flagged and mitigated before impact
Predictive capabilities we deliver
From demand forecasting to risk scoring — systems that act on what they predict.
Predicting volume before it arrives — for inventory, staffing, and capacity planning.
Multi-horizon forecasting combining historical patterns with external signals — weather, events, market data.
Automated reorder triggers and safety stock calculations linked to forecast confidence intervals.
Staffing and resource allocation recommendations aligned to predicted demand curves.
Model evaluation
Time-aware evaluation that reflects production reality.
Standard cross-validation overstates predictive performance for time-series problems because it allows future data to inform past predictions. We use time-aware evaluation — training on past data, evaluating on future data — to get performance estimates that reflect what will actually happen in production.
models deployed with inflated accuracy claims
time-aware cross-validation
AurvikAI predictive analytics — every model ships with explainability and confidence intervals.
Predictive systems in production
Real deployments with measurable business impact.
Logistics demand forecasting
ML demand forecasting using 3 years of historical data, weather patterns, and market signals. Reduced empty miles by 34% — £2M annual saving.
annual saving
Signal validation
We validate the signal exists before you invest in the model.
Before modelling, we test whether the predictive signal you're looking for actually exists in your historical data. Many prediction problems that seem solvable turn out to have insufficient signal. We establish this before investing in model development — saving you months and budget.
INSIGHTS
Thinking worth reading
Ready to predict what matters for your business?
Let's start with the decision you want to improve and work backwards to the prediction that enables it.