# AI Matchmaking Pipeline

Built scalable data pipelines to support AI-assisted matchmaking and future personalization.

## Key Takeaways:

#### Data processing limited pairing accuracy

User data could not be efficiently processed and scored, limiting the ability to generate high-quality matches.

#### We enabled scalable data preparation for matching

A structured data pipeline was introduced to support pairing logic and future AI-driven enhancements.

#### Databricks-based data transformation

User data was ingested into Databricks and transformed into dbt models, enabling scalable scoring and candidate generation aligned with business rules.

#### Foundation established for AI-driven matchmaking

The platform enabled more accurate pairing logic and unlocked future personalization initiatives.

### Continue Reading

### [Governed Metrics for AI](/content/case-studies/semantic-layer-enterprise-metrics/index.html)

Single logic across every interface

### [Compliance Reporting](/content/case-studies/compliance-reporting/index.html)

Reporting automated

### [Audience Segmentation](/content/case-studies/audience-segmentation/index.html)

Targeting efficiency improved