Re-Engineering Content Moderation for Speed, Quality, and Cost Efficiency for a Global Reviews & Ratings Platform

Content Moderation

2X

Growth in practice within 12 months

103%

Gain in productivity in 12 months

60%

Reduction in agent training time

Client Overview

A leading global consumer reviews and ratings platform that enables users to share experiences with brands and businesses, helping companies build trust, manage reputation, and improve customer experience through transparent feedback.

The Challenge

Inline

Navigating the Complexities of High-Volume Content Moderation

The client’s high-volume content moderation and UGC review operations were impacted by an unmanaged backlog, limited forecasting visibility, inconsistent training outcomes, and high regional operating costs. Legacy QA tools provided minimal insights into moderator performance. Simultaneously, fragmented multilingual coverage and manual processes slowed turnaround times, reducing moderation accuracy and constraining both platform safety and operational efficiency.

Sutherland Solution

Integrating AI, Automation, and Human Expertise

Tech Leader

We engineered a comprehensive transformation of the client’s Content Moderation and Platform Safety operations by implementing an integrated model. This model skillfully combines AI-powered moderation, strategic automation, and expert human-in-the-loop reviews.

  • Queue & Workforce Optimization: Implemented real-time queue management, forecasting, and workload balancing to improve productivity and SLA adherence across moderation queues.
  • Bespoke Training Bootcamps: Introduced progressive, scenario-based content moderation training to improve policy accuracy and ramp readiness.
  • HIIT Performance Intervention Model: Cross-functional deep-dive sessions and targeted coaching to resolve quality gaps and improve moderator decision accuracy.
  • Advanced QA & Performance Framework: Deployed a tiered QA scorecard and LevelUp-based grading model to deliver deeper insights into moderation accuracy and agent performance.
  • AI & LLM-Enabled Moderation: Leveraged AI/ML and LLMs for OCR validation, automated tagging, sentiment analysis, ticket summarization, and next-best-action guidance to improve moderation speed and quality.
  • Multilingual Moderation Optimization: Transitioned operations to cost-efficient global locations and deployed our proprietary Translate AI® solution to support multilingual UGC moderation with fewer non-English FTEs.
  • Top-of-Funnel Auto-Moderation: Implemented ingestion-level AI moderation to improve content quality and reduce downstream moderation volume.
  • Policy & Trust Alignment: Partnered with the client on policy refinement and escalation frameworks to improve Trust & Safety outcomes and user experience.

The Outcome

como

Delivering a New Standard of Excellence and Efficiency

The result was a scalable, AI-enabled Content Moderation operation delivering faster decisions, improved accuracy, lower costs, and stronger platform safety across UGC environments globally.

  • Operational efficiency: Optimized moderation queues with real-time forecasting and balanced workloads
  • Faster readiness: Tier-3 training timeline reduced from 5 months to 2 months
  • Cost reduction: Lower total cost of operations through global delivery optimization and AI-enabled multilingual moderation
  • Translation accuracy: Improved AI-assisted translation accuracy from 90% to 95%
  • Quality improvement: Higher moderation quality scores and reduced turnaround time (TAT)
  • Error reduction: 5% decrease in erroneous tickets on the platform
  • Expanded language coverage: 50% increase in multilingual moderation coverage using AI-enabled English-only staffing
  • SLA performance: 100% of tickets resolved within target AHT across Tier 1–3
  • Stronger platform safety: Fewer user and business complaints, improved content integrity, and higher user trust

2X

Growth in practice within 12 months along with additional team setup in India

103%

Gain in productivity in 12 months

60%

Reduction in agent training time for the highest tier work queue