Overview

Engagement overview

A large support organization managed customer interactions across email, chat, calls, social media, and video. These channels generated high volumes of transcripts containing sensitive personal data, making compliance management more important at scale. Time taken for manual quality reviews varied across teams, and knowledge content was spread across multiple platforms, influencing resolution speed. 

To support more consistent compliance and improve operational visibility, the support data environment was modernized. Multi-channel data was brought together into a centralized platform, while automation and AI-driven analysis were introduced to improve transcript quality checks, protect sensitive information, and make knowledge more accessible to agents.

Our client

ISV Australia
Datagamz is an Australian technology company focused on improving customer and employee experience through analytics and AI-driven insights. Its platform, LivedCX.AI, brings together data from contact centers and digital interactions to provide a clearer view of customer journeys and operational performance. By analyzing interactions across channels, Datagamz helps organizations understand experience drivers and identify opportunities to improve efficiency and outcomes. The company works with enterprises looking to enhance service quality, agent performance, and decision-making using data-led approaches.
Our Client

Business objective

Creating a unified, compliant, and insight-driven support data foundation.

01

Centralizing support data from multiple customer interaction channels

02

Ensuring regulatory compliance through automated PII masking

03

Reducing manual transcript quality checks while improving consistency

04

Creating a unified knowledge base to support faster agent resolution

05

Improving visibility into adherence to standard support workflows

Business solution

A centralized data and automation approach was introduced to support quality, compliance, and operational reporting.

  • Multi-channel support data was ingested and transformed using Azure Data Factory and Databricks
  • Structured data models were created to support consistent reporting and analysis
  • Automated PII redaction was applied using spaCy and Microsoft Presidio before downstream processing
  • AI-driven workflows were used to assess transcript quality and identify deviations from defined standards
  • Knowledge content from multiple platforms was consolidated into Azure Blob Storage and used to enhance automated guidance

Business impact

The modernized support data platform delivered improvements across compliance, efficiency, and service quality. 

~60% eduction in manual QA efforts, increasing accuracy, and reducing turnaround time

100% compliance with regulatory requirements ensured by automated PII redaction, boosting customer trust

Improved agent efficiency with unified knowledge access, leading to faster issue resolution

Centralized data platform enabled faster reporting and consistent insights across all customer touchpoints

Higher CSAT scores through consistent, accurate, and efficient support experiences