Case Study
Modernizing Indexing Workflows with a Hybrid AI + Human QA Model
Key Result Highlights
- 60% reduction in internal editorial load per title.
- Maintained legacy editorial standards with measurable cost and time savings.
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Established a hybrid AI + human QA model for
25-title annual volume. - Enabled modernization of indexing workflows while ensuring quality.
- Created a scalable, long-term QA service with transparent cost structure.
The Client
The client is a global education company offering publishing, assessments, and digital learning services to learners across age groups.
The Challenge
The client adopted AI-generated indexing to speed up textbook production across disciplines. While this improved efficiency, they needed a structured, scalable QA process to ensure the indexes met editorial and academic standards across multiple titles each year.
Critical Success Parameters
- Review 2,500+ AI-generated entries and fix common errors like pagination, glossary, cross-references, over-indexing, formatting.
- Set up an scalable editorial QA layer for 20–25 titles annually.
- Ensure accuracy, completeness, and alignment with past editions.
- Speed up the publishing process without compromising quality.
- Create a feedback loop to refine AI prompts and workflows through QA feedback.
Our Approach
- Established an annuity model to deliver ongoing Index QA services.
- Trained a dedicated editorial team to review AI-generated index files using content review, cross-edition checks, and PDF verification.
- Reviewed 2,500+ index entries in 5 business days for the initial title.
- Restored 80+ glossary terms and corrected 100+ cross-references and formatting errors.
- Reduced subject over-indexing by 40% in the first QA cycle.
- Deployed a scalable QA process supporting 25+ titles annually.
- Achieved 1-week average turnaround per title using offshore teams.
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