Built for books that need real editorial judgment
Subject indexing is not just keyword extraction. A useful back-of-book index depends on deciding which concepts matter, which passages deserve grouping, when a subentry is necessary, and how much space an index should occupy in the final book. Generic AI tools are weak at those editorial tradeoffs.
IndexerLabs is designed specifically for that problem. Our subject indexing workflow combines domain-trained models, deterministic validation, and configurable output controls so publishers and authors can generate indexes that are faster, cheaper, and still suitable for production use.
Want to see a subject indexing demo?
Compare a human-produced subject index and an IndexerLabs-generated index in a side-by-side blind test based on a real book.
See what powers it
Read more about IndexLM-1, our subject indexing model trained on more than 1,000 real-world indexes.
Read the model announcement