---
title: "Segmentation with Bias Auditing | AimRank"
url: https://www.aimrank.io/products/segmentation-bias-audit/
description: "Segment your customers and test every segment for disparate impact - targeting lift for marketing, a bias audit report for your DPO. AI prototype from €1,690."
language: en
updated: 2026-10-09
---

[Products · #07 · Risk & Compliance](https://www.aimrank.io/products/)

TIER S BLUEPRINT

# Segmentation with Bias Auditing

Segment your customers and test every segment for disparate impact - targeting lift for marketing, a bias audit report for your DPO.

scaffold from **€1,690**

## How it works

01

### Connect your customer data

Warehouse read-only. Features catalogued; protected attributes and likely proxies flagged before any model runs.

02

### Segment for humans

Interpretable clusters with plain-language profiles, sized and named for marketers - not centroids only a data scientist can read.

03

### Audit every segment

4/5ths-rule disparate-impact tests on gender, age and language, plus proxy detection. Failing features are removed and the model re-clustered.

04

### Ship with an audit report

Every export carries its bias audit report and methodology note. Segments re-audit automatically on every refresh.

## Methodology reference

The published method this build is based on, and what it did in production.

Microsoft

**Microsoft** open-sourced Fairlearn to measure and mitigate group-fairness gaps across protected groups.

Group-fairness auditing · as reported by [Fairlearn ↗](https://github.com/fairlearn/fairlearn)

Referenced for methodology only. AimRank is not affiliated with, endorsed by, or a customer of these companies; each links to its own public source. Sources last checked September 2026.

## The Four Guarantees™ - this build

The scaffold wires all four in. The PoC proves them on your data, as of that day. Only Implementation keeps them true over time: watched, drift-checked, with recourse.

### Measured value

\~20% campaign conversion lift vs unsegmented sends at reference volume; eval gate: silhouette + stability + full bias pass before ship.

### Defensible

4/5ths-rule tests + proxy detection documented per segment; GDPR profiling analysis (Art. 22) and legitimate-interest note included.

### Self-improving

Segments re-audited on every data refresh; new features auto-scanned for proxy correlation; drift alerts when segment sizes shift.

### Yours & everywhere

Runs inside your warehouse, full source. MCP endpoint - your CRM and campaign tools consume segments directly.

## The number, sized honestly

Reference buyer: Lithuanian bank, insurer or telecom marketing team - 50k-500k customers, regular campaign sends, a DPO who has to sign off on profiling.

**\~20%** campaign conversion lift - the agreed number (conservative)

**4/5ths** disparate-impact rule tested and documented on every segment

**100%** of features scanned for proxy discrimination

**\~2 mo** payback at the PoC price, reference volume

## Three ways to own it

| Tier | What you get | Price | |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | |
| **Scaffolding** ★ | The full repo - clustering pipeline, bias-audit harness (4/5ths + proxy scan), audit report generator, MCP server. Reference run on public data. | **€1,690** | |
| **PoC** ★★RECOMMENDED | Clustered and audited on your real customer data - segment quality and the full 4/5ths + proxy audit run end-to-end. Code + bias audit report + quality report yours. No guarantee at PoC. | from**€5,000** | |
| **Implementation** ★★★ | Production: warehouse-native refresh with automatic re-audit, campaign-tool integration, drift alerts, quarterly regulator-ready methodology report - the agreed number (conservative) attaches here. | from **€15,000**\+ from €900/month assurance | |

★ = engagement depth. **PoC is the recommended path**: quality proven on your data before production money. The PoC carries no performance guarantee by design; the agreed number (conservative) attaches at Implementation, informed by the PoC report.

## What we don't promise

**A bias audit is evidence, not absolution.** We test what's measurable - disparate impact on the attributes and proxies observable in your data - and every audit report documents the method and its limits. If your data can't support a fairness claim, the report says so. That honesty is exactly what makes it defensible in front of a DPO or regulator.

## Ready to see your own number?

Request the build: within 48h you get a personal reply with the value sized to your volume.

No commitment · reply within 48h · your data stays in the EU