Stage 01
Review the brief
Clarify what decision the clustering is supposed to support, what the unit of analysis is, and what failure would cost the business.
Outputs
- Decision context
- Unit of analysis
- Risk framing
Process
The workflow is designed to prevent premature certainty. Each stage removes a different type of business and technical error before the result reaches a client, a dashboard, or an API.
Workflow map
The stages are connected for a reason. Each one reduces a different kind of risk before the next begins, so the business is not scaling the wrong geometry or reporting the wrong answer.
Stage 01
Clarify what decision the clustering is supposed to support, what the unit of analysis is, and what failure would cost the business.
Outputs
Stage 02
Inspect density, distance behavior, outliers, drift, and whether Euclidean, cosine, or centroid baselines are likely to fail before any workflow is approved.
Outputs
Stage 03
Turn the assessment into a concrete methodology competition with clear success criteria, validation choices, and an explicit delivery target.
Outputs
Stage 04
Run the selected method family against the real data constraints and verify that the cluster structure remains interpretable and decision-relevant.
Outputs
Stage 05
Package the clustered data, assignments, confidence, diagnostics, and visuals in a form the client can use and explain internally.
Outputs
Stage 06
If the methodology is stable, package the approved logic as an API and define drift, refresh, and reassessment rules before ongoing use.
Outputs
Start here
If you send a dataset brief, the first response will explain which stages matter most and what the plan of action would be.
Send the brief, get an assessment, and receive a plan of action within one business day.