Verifying 20,000 Index Locators at Scale
A bad locator is the most damaging failure in an index. We solve this by requiring structured evidence for every claim and validating it through deterministic and adjudicative stages.
Read Full Post →Insights, updates, and research from the intersection of AI and indexing.
A bad locator is the most damaging failure in an index. We solve this by requiring structured evidence for every claim and validating it through deterministic and adjudicative stages.
Read Full Post →We tested how well major LLMs can prune a 7,400-term candidate index down to 1,000 terms while retaining the same topics selected by a professional human indexer.
Read Full Post →We trained an AI on 1,000+ publicly available back-of-the-book indexes. The resulting model, IndexLM-1.0, consistently outperforms general-purpose LLMs on subject indexing.
Read Full Post →IndexerLabs is pleased to announce the launch of ScriptureBench, the first comprehensive evaluation test set for scripture indexing, and ScriptureLM-1, our domain-fine-tuned model.
Read Full Post →Automated book indexing is not only about extracting page numbers. A strong AI book indexing tool must first know what belongs in the index, and then index it correctly. We believe those are two separate problems, and that they should be solved separately.
Read Full Post →Quick Check gives you a fast, macro-driven review workflow for locator verification, designed to remove friction from the editorial review process.
Read Full Post →Instead of only returning a separate index as plain text, we can now insert embedded index markers directly into the original Word document itself.
Read Full Post →Learn how to create a book index automatically with AI-assisted indexing software, page-aware locators, review tools, and publication-ready export.
Read Full Post →Book indexing cost depends on page count, complexity, deadline, format, and review needs. Learn how professional indexing is priced and how AI changes the economics.
Read Full Post →ChatGPT can suggest entries and subentries, but creating a reliable book index requires locator verification, revision, pruning, and a structured indexing workflow.
Read Full Post →An AI book index generator has to do more than generate terms. Learn what separates a weak keyword list from a structured, reviewable, publication-ready book index.
Read Full Post →AI book indexing is not keyword extraction. Learn how AI can help authors and publishers create structured, page-aware, reviewable back-of-book indexes.
Read Full Post →Creating a high-quality book index has traditionally been one of the most time-consuming parts of publishing a nonfiction book. IndexerLabs is an AI book indexing tool designed to help you create a professional back-of-book index quickly and affordably.
Read Full Post →A professional index should be treated as a structured editorial product, not a loose list of keywords. Specialized AI for book indexing makes this process faster, more affordable, and easier to review.
Read Full Post →A professional back-of-book index should do more than generate keywords. Learn how specialized AI workflows support real subject indexing, accurate locator verification, and output length control.
Read Full Post →Learn how to index a book step-by-step. This complete guide explains professional indexing methods, common mistakes, and how AI can generate high-quality book indexes faster.
Read Full Post →Indexing a book is a difficult and tedious task. However, there is now an opportunity to revolutionize the indexing process and make it more accessible to authors of all backgrounds and budgets.
Read Full Post →Scripture indexing is deceptively simple until you encounter the real-world complexities of semantic context. Explore why automation has failed until now.
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