Forecasting, scoring and anomaly detection.
Predictive AI
Classical machine learning where it beats a language model: demand and capacity forecasting, risk and propensity scoring, anomaly and fraud detection, and recommendation engines.
- Typical timeline
- 8–14 weeks
- Engagement model
- Fixed scope
What you get
- Demand, capacity and revenue forecasting
- Risk, credit and propensity scoring
- Anomaly and fraud detection
- Recommendation and next-best-action services
- Backtesting and drift monitoring
What changes
- Forecasts with stated confidence intervals
- Models monitored for drift rather than assumed stable
- Decisions that can be explained to a regulator
Architectures this usually produces
The assessment decides from your answers — these are the patterns it most often lands on for this service.
Classical ML
Questions people ask
- Why not just use an LLM for this?
- Because for numeric prediction a gradient-boosted model is usually more accurate, far cheaper and explainable. We use language models where language is the problem.
Considering predictive ai?
Run the free assessment first. It scores feasibility against your own data and volumes, and tells you what it would cost before anyone quotes you.