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Ollang Agentic Localization is Ollang’s own translation workflow and the default provider for every translate step. It is the only entry in the translation catalog that is a pipeline rather than a single model: agents are selected dynamically per segment based on content type, domain, and structural complexity, then their output is reconciled. That design targets the gap between translation and localization. A frontier LLM will translate a marketing line accurately; a localization workflow decides whether the line should be translated at all, or replaced with something that lands the same way for a different audience.
Available in Ollang Workflows — Document Translation, Subtitle Translation, and AI Dubbing. See the Translation catalog for the full comparison against 25 other engines.

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Models

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Key capabilities

  • End-to-end localization across text, documents, subtitles, and dubbing scripts in 100+ languages, from a single provider selection.
  • Dynamic multi-agent selection — different agents handle different content, which is what carries quality through domain-specific and structurally complex material that a single prompt would flatten.
  • Culturally aware output — idiom, register, and cultural reference are adapted rather than transliterated.
  • Format and structure preservation across the document types Ollang ingests.
  • Steerable through Memory, glossaries, and custom instructions, which apply to the whole workflow rather than to one model call.

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Agentic Turbo

Agentic Turbo is the same shape of workflow tuned for throughput, while retaining contextual quality that pure machine translation does not reach. It suits high-volume work — large subtitle backlogs, bulk document sets, and any pipeline where turnaround determines scope.
Agentic Turbo is experimental. Benchmark it against your current provider in a separate Folder before moving a production Global Workflow onto it.

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Where it fits in a workflow

Agentic Localization is the default because most Orders are judged on localization quality rather than translation accuracy alone. It earns its place on content where the output is customer-facing and the phrasing carries value. A common configuration is Agentic Localization as the Global Workflow default, with Agentic Turbo or DeepL overridden at Folder level for bulk content.

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Trade-offs

  • Latency — a multi-agent pass does more work than a single model call, which shows up in turnaround on large batches.

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Reference