Prediction design
Define the outcome, prediction window, available inputs, and actions a user can take on the result.
Intelligence in practice
Machine Learning & Predictive Analytics
Machine learning development for service businesses and portfolio companies. Build demand forecasts, operational scoring, and predictive decision support.
Discuss your AI project ↗Keep evaluation separate from training
Historical recordsDefine the outcome and available inputs
Training dataFit candidate models
Model developmentCompare with a simple baseline
Held-out evaluation dataKeep separate from model fitting
Performance checkMeasure error on unseen cases
Operational decisionUse predictions with context and uncertainty
Predict something a person can act on.
Predictive systems are useful when there is a measurable question and data that reflects the decision environment. Blake Technologies develops models with a clear baseline and evaluation plan. We examine how historical records were produced, whether outcomes are recorded consistently, and how predictions will change a workflow. The result is decision support with known limitations, not a promise to predict every event.
For service businesses, predictive analytics can support demand planning, resource allocation, and investigation of unusual operating patterns. Private equity firms can evaluate forecasting or scoring opportunities across portfolio companies, provided the underlying data supports the comparison. We define how a prediction changes a business decision before selecting models or building integrations.
Define the decision before selecting a model
Model development
Prepare datasets, compare appropriate baselines, and separate training from evaluation to reduce misleading performance estimates.
Operational monitoring
Track prediction quality and changes in incoming data, with a plan for review and model updates.
A forecast is an input to planning
Your engagement outputs
- A documented dataset and evaluation approach
- A model or scoring service integrated into the agreed workflow
- Performance reporting and model maintenance recommendations
Scope, schedule, team, and support are agreed for the engagement.
A useful distinction
What if there is not enough useful historical data?
We may recommend better data collection, a simpler rule-based approach, or a limited feasibility study. Adding a complex model does not compensate for an undefined outcome or unreliable records.