Multi-Language AI Translation
100k+ Bible verses translated across five languages using AI
A Bible platform serving 50K+ users, providing word-for-word Hebrew and Greek text alongside Bible translations to support deeper scripture study and understanding.
- Client
- Yahuah Bible Platform
- Surface
- Scripture, concordance, CMS, UI
- Build
- Python, Gemma 4 31B, RunPod
- Service areas
- Five of six
The result — one automated pass per language
- Hours saved
- 500+
- Translation cost saved
- $5k+
- New users reached
- 2k+
01 — Brief
The problem
The client runs a Bible platform built around a proprietary translation: Hebrew and Greek source nouns are preserved and woven directly into the English text, rather than translated away. That fidelity already worked for English and Spanish readers. The client wanted to extend it to French, German, Italian, and Brazilian Portuguese.
The work being replaced
Re-doing the translation by hand in each new language, one language at a time.
What replaced it
A single automated pass per language, staged for an administrator to approve before a reader sees it.
02 — Constraint
Why the obvious path doesn’t work here
Three constraints ruled out a standard localization vendor.
Constraint 1
FidelityOff-the-shelf translation APIs strip the thing that makes the text theirs.
General-purpose MT has no concept of a “sacred word” that has to survive translation untouched and still read naturally in the target language.
Constraint 2
ScopeThe translation surface is bigger than the Bible text.
Strong’s Concordance, CMS pages, and UI language strings all needed the same treatment, and none of them share a data shape — verses are short and structured, CMS pages are HTML, language strings are key-value pairs.
Constraint 3
AccuracyZero tolerance for invented text.
A theological work can’t absorb a model’s hallucination the way marketing copy can. Every verse, every word-usage count, every sacred-word placeholder had to survive the round trip exactly.
03 — Recommendation
Our solution
Build vs buy
No vendor API meets the placeholder-fidelity requirement, and a generic MT pipeline cannot be steered into one. The call was to build.
The loop, end to end
AI Strategy & Roadmap
Connect the existing admin panel to a one-time conversion pipeline on rented GPU compute, rather than standing up permanent infrastructure for a job that runs once per language. Content leaves the platform, reaches the model, and returns on its own — collapsing months of manual translation into a single automated pass. Sized to run on an RTX 6000 Blackwell instance for roughly $100 of RunPod credits.
AI Stack
A custom Python pipeline rather than an off-the-shelf localization tool, with Gemma 4 31B as the translation engine — it carries cross-language conversion and the sacred-word fidelity the platform is built around, which a general-purpose service cannot.
AI Governance
Dedicated API endpoints, scoped so the pipeline can reach only the data it needs and nothing else, and a DAG-based orchestrator so the data hierarchy survives every stage instead of being flattened by a linear script. Translated content returns into a staging area in the admin panel rather than straight to production: an administrator reviews it against the source and approves it before a reader ever sees it, so a human stays in the loop on every batch. We also advised a status bar in the panel showing where the pipeline is during a run, so the team can watch progress and catch a failed stage while it is happening rather than discovering it once the run ends.
AI Evaluation
Bench the candidates before committing to one. Gemma 4 31B came out ahead, and the extracted sacred-word dataset became the ground truth the final output was scored against.
TranslateGemma 27BGemma 4 31BAI Security
Authenticated API calls on every hop, so only clean, verified data enters or leaves the platform.
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