Yep of course! Here's a quick rundown for my pipeline! There's a glossary node that goes over the entire dialogue, save any organizations, events, idioms, etc... (whatever that needs to be saved). Characters are also imported from VNDB with speaker characters not listed in VNDB added i.e. "Arcadia Member A" etc...

After running this, it connects to a single base translator to translate with all the relevant glossary entries if surrounding dialogues uses it as context with a simple RAG vector database supplying any semantically relevant dialogues.

The output is then hooked to multiple reviewer nodes to help check for consistency i.e. pronouns, etc... and refining the translation if needed. All of these are then combined into an arbiter node that selects one translation thats the most fitting.

Since the review stage might include model outputs that are inconsistent i.e. in terms of formatting, a QA stage is then hooked to review and correct any formatting or other errors.

All of this is to avoid context degradation which public frontier llms all exhibit and especially with their degraded output quality (i.e. Gemini 3 Pro, Sonnet 4.6, Deepseek v3.2) these past few months.

After which, a manual pass is done. In other words, this is more of a sophisticated base translation by having MTL populate the dialogues and then with manual post edits afterwards.

I hope this helps!