babellens.ai
An AI-powered Bible app platform — web, iOS, and Android — with interlinear text, manuscript-aware AI commentary, and a proprietary translation built by a six-layer Claude pipeline with integrity checks at every stage.
Bible translation is a fidelity problem. Translators make thousands of choices across thousands of manuscripts — how to render contested terms, what theological frame to apply — and none of it is visible to the reader. Existing tools pick one of two bad UX modes: hide the complexity behind a single translation, or surface everything in a format that requires graduate training to navigate. The gap was a product that let a serious but non-expert reader work with primary sources, scholarly context, and AI commentary in one coherent interface.
Ship a freemium SaaS across web and native mobile from a single React codebase. The free tier gives readers interlinear text and AI commentary grounded in a RAG corpus of historical scholarship. The Scholar tier unlocks the BLV — a proprietary translation generated by a multi-layer Claude pipeline that routes by genre (Hebrew prose, Hebrew poetry, Greek NT), enforces manuscript fidelity rules, and runs six integrity checks before a verse reaches the user. Claude Sonnet handles the high-judgment generation work. Claude Haiku handles the structured verification tasks where cost matters at scale. Nothing releases without passing the integrity stack.
- Interlinear reader — tap any verse, see the original language text alongside translation with Strong's numbers and glosses
- AI commentary — RAG-grounded synthesis across a scholarly corpus, organized into six thematic sections per verse; sources stripped of author and publisher metadata before generation
- BLV translation — genre-aware, manuscript-faithful translation generated by a six-layer integrity pipeline; Scholar-tier only, rendered as an overlay without blocking the base text
- Real-time Q&A — verse-level chat with streaming responses; same hermeneutical constraints enforced as the commentary layer
- Audio Bible — all 1,189 BSB chapters pre-generated and served from CDN, not real-time
- Dual-payment compliance — Stripe on web, RevenueCat (StoreKit / Google Play Billing) on native; both reconciled against one subscription table
- Web + iOS + Android — single React codebase, Capacitor native shells, Xcode Cloud CI for TestFlight and App Store builds
Live on web, iOS, and Android — a single codebase, three platforms, six integrity checks per verse, running in production.
React 18 · TypeScript · Vite · React Router v6 · React Query
shadcn/ui · Tailwind CSS
Supabase — Postgres, RLS, Edge Functions (Deno), pgvector
Claude Sonnet — generation quality, genre routing, manuscript fidelity
Claude Haiku — six-layer structured verification, escalation logic
Claude Haiku — RAG-grounded synthesis, streaming Q&A
Stripe (web) · RevenueCat (iOS + Android)
OpenAI TTS · Cloudflare R2 — pre-generated, CDN-served
Capacitor · Xcode Cloud CI
Vercel (frontend) · Supabase (functions + DB)
Three problems that don't show up until you're in production. Multi-layer AI integrity at scale: six Claude calls per verse, each logging to its own audit table, with hard violations escalating to a human review queue and soft violations releasing with metadata flags — so every generated verse is observable, correctable, and auditable without re-running the pipeline from scratch. Dual-payment subscription coherence: Stripe and RevenueCat write to the same subscriptions table, the client checks entitlements in the right order to prevent stale auth from causing silent fallbacks on iOS cold starts, and the backend verifies tier server-side on every BLV request. BLV overlay without blocking render: the base translation always loads first and renders immediately; the BLV loads per-verse in the background and slots in when ready — users never see a half-loaded chapter.
Babel Lens is the proof that AI integrity architecture isn't just a concern for medical or legal software. Any domain with high-stakes output — translation, scholarship, compliance, finance — needs observable generation, auditable failures, and graceful degradation. The six-layer BLV pipeline is a design pattern, not a Bible project quirk. The same structure applies anywhere you need to know not just what the model said, but whether it was right.