Two layers that match the right tool to each kind of text: instant offline word packs for 20+ languages, plus metered Azure AI for live conversation translation — cached to near-zero cost.
The agency's clients operate in Arabic, Urdu, Spanish and a dozen other languages, but GoHighLevel's interface is English-only. Machine-translating everything via API would cost a fortune and risk rewriting customer data (a contact named “New” must never become “جديد”). And conversation messages — unique, real-time text — can't be covered by any dictionary.
A two-layer architecture that matches the right tool to each kind of text:
Hand-curated dictionaries for 20+ languages swap every known UI string client-side. A DOM TreeWalker with strict block-lists never enters conversations, contact records or editable fields — customer data is untouchable by design. Full RTL support (Arabic, Urdu, Hebrew, Persian) via unicode-bidi: plaintext.
For dynamic content the packs can't know, batches go to Azure AI Translator through a server-side proxy (keys never reach the browser). Every company gets a character-credit meter — 10,000 free, atomic Mongo deduction, top-ups — and translation only runs where a per-location toggle is ON.
An L1 in-memory + L2 Redis cache keyed by text (not by tenant) means once any agency pays to translate “Launchpad” into Arabic, every other agency gets it free forever. Client-side dedupe, batching, viewport-priority and script-detection skip keep requests minimal.
Dual TTLs separate concerns: UI-like strings cache 30 days; conversation-like text only 72 hours (privacy). A Redis sorted set tracks the most-demanded strings, creating a promotion queue — hot strings graduate into the free static packs, so the paid layer shrinks itself over time. Per-company sliding-window rate limits stop runaway tabs from draining credits.
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