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Automated Lore & Continuity
For Massive Fiction Series

We extract characters, timelines, and lore from stories with more than one million words. Our narrative engine helps web novel platforms keep readers engaged and helps studios evaluate IP for adaptations.

Narrative Output: Pathway Selection Matrix
STATUS: LIVE_DATASET
Dataset Preview: Character Pathway choices across Lord of the Mysteries, Vol. 1–8
Lord of the Mysteries Pathway Selection Matrix Chart
*Series Total represents unique characters across the entire series, not a simple sum of volume counts (since characters survive across volumes or undergo pathway selections). Disclaimer: Independent narrative analysis. Lord of the Mysteries and all related terminology are property of Cuttlefish That Loves Diving / Qidian.
Engine Parsing Status
NOW PARSING: Primal Hunter Vol 2 · QUEUED: Defiance of the Fall Vol 3 · COMPLETED: Dungeon Crawler Carl (515 chapters indexed) · NOW PARSING: Primal Hunter Vol 2 · QUEUED: Defiance of the Fall Vol 3 · COMPLETED: Dungeon Crawler Carl (515 chapters indexed)
Our Vision
"We are mapping the DNA of long-form fiction. By transforming sprawling narratives into structured, queryable data, we enable platforms and creators to scale their universes while keeping readers deeply engaged."
RR
Radames Roriz
Founder, EnrichReader

How We Help Businesses

We build structured lore databases to help platforms keep readers engaged and help studios evaluate story value.

🌐

Serialized Platforms

License our engine to add interactive character guides and timelines to your app. Helping readers remember characters reduces drop-offs on long web novels.

Goal: Keep readers on platform
📚

Saga Authors & Publishers

Turn your book catalogs into interactive reading experiences. The system builds spoiler-free directories automatically, and readers can help correct lore details.

Goal: Auto-create lore guides
🎬

Adaptation Scouts & Studios

Evaluate stories quickly for television, film, or game adaptations. The engine audits cast size, how often characters appear, and plot complexity in seconds.

Goal: Speed up story audits

How the Engine Works

We replace slow manual work. The engine reads massive novels and turns them into organized, searchable databases.

Massive Narrative Scaling

The engine runs code directly on the book's text. It extracts detailed relationship data for every chapter. The resulting database is often larger than the original book text.

Human Validation

Although the engine works automatically, our editors perform asynchronous double-checks on the final generated data to ensure complete accuracy.

1

Name & Alias Groups

The engine scans the entire book to find names, group aliases, and track characters, items, and locations.

2

Page-by-Page Timelines

Our system reads page-by-page to track timelines, when characters disappear, and events like deaths or faction changes.

3

Traceable Context

We link every data point back to the exact sentence and chapter in the book. You can click any character detail to see the original context and verify it.

Extraction Pipeline: Multi-Stage parsing
1. Book Parser Ingest & Format Text 2. Entities Recognition Initial Name Discovery 3. Chapter Extraction Chapter by chapter (n times) 4. Merge Similar Merge similar entities 5. Compile Build Narrative Index 6. Stats Generate Datasets

Narrative Datasets

Explore how the engine parses millions of words to compile structural datasets.

View All Catalog Reports
*Dungeon Crawler Carl, Lord of the Mysteries, and Circle of Inevitability are the property of their respective creators and publishers. Case studies are independent, unaffiliated narrative analyses.

Proven in Use

We prove our data works by showing it directly to readers in our app. Readers interact with it and submit corrections to help improve our engine.

Reader Corrections

Data accuracy improves every week as your users actively validate character and lore details, reducing editorial QA costs.

Better Book Completion

Readers with character guides finish long series at higher rates, directly improving platform retention metrics.

FAQ

Answers about our parsing engine, database APIs, and feedback loops.

The engine processes text page-by-page using deterministic parsing algorithms instead of generic AI prompts. To guarantee accuracy, our editors perform asynchronous double-checks on the final generated data. This validation loop helps refine our parsing rules continuously while ensuring clean, production-ready outputs.
Most clients integrate without writing code — we provide API access plus JSON/CSV exports for teams building custom tooling. Web novel apps use our API to show character guides and lore indexes inside their apps. We also provide publishers and film studios with structured data exports and continuity reports.
We built the engine to parse very long stories. It easily processes series with over 1,000 chapters (two million words), where tracking characters manually is too difficult.
We license our technology and customize pricing based on what you need. Email our team at sales@enrichreader.com to discuss integrating the engine or requesting a catalog audit.

Ready to audit your catalog?

Get custom API access or scope your fictional universe. We'll analyze your catalog and deliver a full report in 24 hours.

Request Engine Demo

Contact Us

Get in touch to request a demo of the engine, get API access, or schedule an audit for your book catalog.

Schedule a call

Want to discuss integration? Book a 15-minute call with our founding engineer.

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