Direct definition: We use AI to turn complex data into diagnostics, predictions and actionable decisions. Measurement foundations, statistics, visualization and AI models are combined to increase speed without sacrificing rigor.
The challenge
Augmented analytics is not asking a model for conclusions without context. It requires defined data, clear questions, validation, explanation and a process for action. We design systems that separate signal from noise, communicate uncertainty and connect each insight to a decision.
How we work
- Definition of objectives, decisions, sources, quality and baseline.
- Modeling, analysis, visualization, evaluation and explanation.
- Integration into dashboards, alerts, workflows and decision cycles.
Application example
A retailer wants to anticipate demand decline. Sales, campaigns, inventory, price and calendar data are integrated; the model estimates scenarios and detects anomalies. The dashboard explains relevant factors and recommends questions, not automatic orders. Buying and marketing review scenarios, document the decision and compare forecasts with outcomes to improve the next cycle.
How it is measured
- Accuracy and calibration
- Time from data to decision
- Team use of insights
- Incremental impact of decisions
Frequently asked questions
Is a large volume of data required?
Not always; the approach adapts to volume, quality and the decision required.
Does AI replace the analyst?
No. It increases exploration and speed; analysts provide context, validation and judgment.