Predictive Analytics
How this engagement works
We build predictive systems that answer specific business questions: forecast demand, detect risk, and prioritize actions. Models are evaluated against decision metrics and shipped with monitoring so performance doesn’t silently decay.
We implement feature pipelines, training/eval workflows, drift monitoring, and deployment patterns (batch or real-time). We define baseline heuristics and compare to model lift before rollout.
What changes
- Forecasts and scores that map to real operational decisions
- Reduced surprises via monitoring and drift detection
- A repeatable pipeline for retraining and iteration
What we deliver
Scope is written in concrete capabilities, so the team knows what ships and how it will be judged.
- Business-aligned metrics (precision/recall, calibration, cost of error)
- Data drift and model performance monitoring
- Batch scoring or real-time inference APIs
- Backtesting + baseline comparisons
- Experimentation plan for model rollout
Where it fits
- Demand forecasting for inventory and staffing
- Churn/renewal risk scoring with next-best-action
- Lead scoring for sales prioritization
- Anomaly detection for ops and finance
The working rhythm
- 1
Define decision and cost-of-error
- 2
Data audit and label quality checks
- 3
Model baseline + backtest
- 4
Deploy with monitoring and guardrails
- 5
Iterate with feedback loops
Hand-off package
Model system
- Feature + training pipeline
- Evaluation report with baselines and lift
- Deployment pattern (batch/real-time)
Operations
- Monitoring dashboards (drift + performance)
- Alerting + incident playbooks
- Documentation for ongoing iteration
Typical stack
Questions we hear first
Related services
Start with the smallest useful version
Tell us the outcome you need in the next 90 days. We will map it to a scope, a cadence, and the services that should sit next to this one.