---
id: coverage.case-segmentation
title: Case Segmentation
summary: Segment cases into meaningful behavior groups so benchmark coverage is explainable.
kind: concept
product_area: coverage_engineering
status: stable
updated: 2026-08-23
canonical: /docs/coverage-engineering/case-segmentation
---

# Case Segmentation

## Definition

Case segmentation is the act of reading a Case population by meaningful behavior slices instead of only as a total count. In Teammately, reusable Dimensions and ontology values provide governed segmentation axes; Project Topics and Case Construction Patterns provide additional intent; Benchmark Dataset Representation shows how selected Cases occupy those axes.

A segment is an analytical grouping, not a new Case object or a hidden approval state.

## Why it matters

Aggregate benchmark results can hide concentrated success and consequential absence. A candidate may score well overall while failing every Case with missing evidence, a particular jurisdiction, or a high-impact exception. Segmentation makes those differences inspectable and gives Coverage Stories a concrete population to address.

## Where it appears in the product

Define reusable axes in **Coverage Facets → Dimensions & Ontology**. Classify Cases against those values through supported Case and coverage workflows. Use **Benchmark Datasets → Representation** to inspect the current selected population. Use Coverage Management when a sparse or absent intersection needs a Story, Case Foundry work, or an Expert Contribution.

## Artifacts it affects

Keep three identities separate: the reusable Dimension, the ontology value assigned to a Case, and the Benchmark Dataset membership being analyzed. Changing a value can alter current Representation without modifying an older Snapshot. Adding a Case can change counts without changing the segmentation model. A filter is a view over evidence, not a new evidence object.

## Operational check

Choose axes that describe material behavior, context, risk, or source conditions. Check distinct Case counts as well as assignments, inspect unclassified and conflicting values, and avoid interpreting overlapping segments as additive totals. When reporting a result by segment, name the Benchmark Version whose Case assignments support the analysis.

{% example-demo title="Finding a hidden source-authority gap" %}
The dataset contains many high-impact Cases and many Cases with superseded sources, but Representation shows almost no Cases where both conditions occur together. The team creates a Coverage Story for that intersection. Case Foundry prepares candidates, reviewers confirm their realism, and a later Snapshot records the expanded segment. The Dimension definitions remain reusable across other Benchmarks.
{% /example-demo %}

## Related workflows

{% related-card-grid title="Related workflows" %}
- [Dimensions and ontology](/docs/coverage-engineering/dimensions-ontology)
- [Coverage dimensions](/docs/object-model/coverage-dimensions)
- [Coverage Gaps](/docs/coverage-engineering/coverage-gaps)
- [Product quickstart](/docs/quickstart)
- [Task index](/docs/operating-manual/task-index)
{% /related-card-grid %}

## Source confidence

Code-backed: the Representation explorer and Dimension classification types establish facet-based Case grouping, assigned and missing values, and benchmark-scoped distribution views. Coverage significance and gap priority remain accountable human judgments.