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TextSetu can translate with AI — draft translations automatically using Claude, GPT, Gemini, or DeepSeek, tuned to your brand and terminology. Every AI translation in your organization routes through a reusable, named configuration called an AI engine, so the machine translates the way your team already does.

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What an AI engine is

An AI engine is an org-scoped, reusable configuration that defines how AI translates for you: which providers and models to use, how to sound, the rules to follow, and which terminology to honor. You set it up once and every AI translation — the per-key button, the bulk page, the API — runs through it, so the output stays consistent across projects. An engine composes each prompt from five layers, listed here from lowest to highest precedence. When two layers disagree, the higher one wins. Because the glossary and TM layers sit at the top, AI and human translators see the same terminology — see Glossary and Translation Memory.

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Providers, BYOK, and credits

An engine can use Anthropic (Claude), OpenAI, Google (Gemini), or DeepSeek. There are two ways to pay:
  • BYOK (bring your own key) — connect your own provider API key. Usage is free and unmetered by TextSetu; you’re billed by the provider directly.
  • TextSetu platform keys — use TextSetu’s provider access, metered against a prepaid credit balance.
You can mix both across engines. See Cost control below.

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How AI translations land

AI writes respect the same approval workflow as human edits — review is a per-run choice, not a hard gate:
  • Approval on → AI translations land as proposals in the review queue, exactly like a translator’s edit. An approver can instead choose apply directly to publish them live.
  • Approval off → AI translations go live immediately.
You can also target a branch instead of main — the branch merge then becomes the review gate. Protected main branches are respected: direct-to-main AI writes are refused just as manual ones are. Every AI-written value is marked AI-generated so you can tell machine drafts from human work.
On approval-required projects, running AI into a branch (or as proposals) gives you a clean review step before anything ships — the AI does the first pass, a human signs off.

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Running it

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Per key

In the translations grid, use the Translate with AI button on a key to draft one or more languages for that key. How the result lands (live, proposal, or branch) follows the project’s AI-translation settings.

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In bulk

The bulk AI page translates many keys at once. Choose your target languages and narrow the work with filters:
  • Only untranslated — keys with no value yet in the target language.
  • Only outdated — values flagged outdated because the source changed.
  • By tag — restrict to keys carrying a tag.
  • By key prefix — restrict to a scope of the key tree.
Candidates are filtered in SQL before any model is called, and requests are batched, so you only pay for the strings that actually need translating. Bulk runs are asynchronous — kick one off and track its progress while it works.

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Cost control

Cost is the binding constraint, so TextSetu is conservative by default:
  • BYOK is free and unmetered.
  • Platform runs are pre-flighted against your credit balance — a run you can’t afford is refused up front, never stopped mid-run partway through.
  • Prices are versioned, so historical usage stays explicable after a price change.
Combined with the pre-model candidate filtering above, you only ever spend on strings that need translating.

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Reports

TextSetu reports both usage and quality for AI translation — translations, spend, review scores, and response rate — broken down by provider, model, language, and engine, so you can see what each configuration costs and how well it performs.

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API & MCP

Everything above is automatable. The REST API exposes AI translation under /api/v1:
  • GET /projects/{projectId}/ai/engine — the resolved engine for a project and whether AI translation is available.
  • POST /projects/{projectId}/ai/translations — start an async run (returns a jobId); supports the same filters, a ?branch= target, and the approval behavior described above.
  • GET /projects/{projectId}/ai/translations/{jobId} — poll a run’s status plus translated / skipped / failed counts.
For AI agents, the MCP server exposes the same capability as tools: ai_engine_info, ai_translate, and ai_translate_status.
Related: Translating · Approval Workflow · Branches · Glossary · Translation Memory