You write the next chapter. SynapTale keeps the story memory up to date and uses it to publish consistent translations.
The load on you is deliberately close to zero. The system runs fully autonomously on the chapters you already publish. Everything it builds stays yours to edit, from the wiki and the graph to translation decisions, whenever you want to step in.
SynapTale works through a story chapter by chapter. It autonomously builds and updates a living wiki and a temporal graph of characters, relationships, events, and knowledge. The translation pipeline uses that record as its memory, so a detail introduced hundreds of chapters ago is still available when it matters.
A new chapter
Wiki and graph update
Context-aware translation
Release
Seven real screens from the living wiki of the demo book. Every card opens that exact spot in the live demo.
Run a novel through a raw model chapter by chapter and you get what everyone has seen. Names drift, voices flatten, callbacks are missed. SynapTale is engineered specifically against that failure mode.
The pipeline runs on the strongest models available today, currently Claude Opus 4.8. And it is not one model asked to translate. Separate specialized agents draft, validate, and edit every chapter.
A novel algorithmic layer backs the models with deterministic signals for alignment, terminology, and grammar-sensitive spots.
Every chapter passes a dedicated quality gate that checks fidelity, hallucinations, canon, and voice. Suspicious fragments get an extra audit, and a periodic editor re-reads recent chapters with fresh eyes to catch what slipped through.
A separate canonization layer fixes the translation of names, terms, and catchphrases in a decision ledger before they are used, so the same sword, city, or nickname reads the same in chapter 400 as in chapter 4.
The graph tracks the linguistic relationship between every pair of characters and keeps a language profile for each character across the whole story, from register and forms of address to verbal habits. Dialogue keeps its voice in every language.
That is not a claim of perfection. Errors still happen, but at a rate comparable to a quality human translation, which has been the bar from day one.
The public demo covers the first hundred chapters of the Oz series, about 180,000 words read end to end. The same architecture is built for series in the millions of words. Each chapter is translated from a bounded, chapter-aware context compiled from the graph, so quality does not degrade as the series grows. When you publish a new chapter, the wiki update and translations can be ready within a few hours.
Translation is ready today in German, Spanish, and Ukrainian. You can read all three in the demo, and the pipeline is built to extend to more.
The graph and codex start private. They are your working instrument for continuity, planning, and checking your own canon.
Inspect any character, relationship, or event as it stood at any chapter, across the whole history of your world. Private by default.
Open the portal to readers as a spoiler-safe fandom wiki. Reader access is chapter-aware, so each reader sees the world exactly as it stood at their current chapter, and nothing from later chapters leaks through.
No forms and no commitments up front. The first step is a private demo built from your actual chapters, because the only honest way to judge this system is on a story you know by heart.
With your go-ahead, I take the first 10–20 chapters of your series. That is the whole ask. Nothing signed, nothing exclusive, no cost to you.
The system reads those chapters and builds the full experience on this portal. The story graph, the codex, and translated chapters appear under a private account visible to you and no one else.
Browse the world record, read the translations, check the details you know best. If it does not convince you, it ends there. If it does, we discuss terms.
At minimum you get a living wiki-fandom covering every chapter of the chosen story, plus translation into the first three languages. From then on every new chapter is picked up the same way, with the update and translations ready within hours.
All rights remain with you. I receive only a license to sell the translations we agree on, scoped to the languages we pick together. If you end the partnership, new sales stop. Your original text is never mine to sell.
I cover translation, distribution, and all related costs. If the translations earn nothing, you lose nothing.
I receive an agreed share of the profit from translation sales. I only earn when the translations sell, which gives me a direct reason to maximize their sales. The profit split can be handled through a dedicated Patreon page or another system you prefer.
Where the translations are sold is part of our agreement, not something that happens to you. We agree the channels for each language before anything goes on sale, and nothing appears anywhere we have not agreed.
SynapTale is not a wrapper around a single language model. It combines specialized agents, programmatic NLP, temporal graph algorithms, deterministic checks, and several layers of verification and validation. The architecture draws on current research in natural language processing, knowledge representation, long-context systems, and literary translation, then engineers those ideas into one working pipeline.
The prescan cleans source formatting while preserving meaningful narrative structure. It separates chapters, removes non-story material, maps story arcs, and creates a summary for every chapter.
Before extraction begins, the system studies a wide span of the series and defines the kinds of characters, relationships, concepts, and facts it needs to track. This lets it follow a detail from chapter one even when its importance becomes clear hundreds of chapters later.
Several chapter-level extractors and retrospective validators build a temporal event graph. It keeps entities, relationships, reified events, changing states, and character knowledge on one timeline, so context can be reconstructed as it existed at any chapter.
A two-phase decision process fixes translations for names, terms, catchphrases, and other recurring language. Linguistic profiles capture character speech patterns and idiolect so voice and terminology stay consistent across hundreds of chapters.
For each chapter, the graph is compiled into focused story, language, and translation-policy context. A translation agent creates the draft. A separate quality gate checks fidelity, hallucinations, canon, voice, and linguistic consistency. Additional audit layers can review suspicious fragments when needed, while deterministic NLP checks and post-processing catch structural and language-level defects before release.
If you want that private demo, send me a link to your series and tell me which languages matter to you. It is free, it stays private, and it commits you to nothing.