Hera AI icon
Hera AI icon

Hera AI

Enter the future of localization with Hera AI, InText’s automated post-editing, LQA and QE tool that takes context into account.

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Cost / License

  • Subscription
  • Proprietary

Application type

Platforms

  • Windows
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Properties

  1.  Privacy focused

Features

  1.  No Coding Required
  2.  No Tracking
  3.  Ad-free
  4.  AI-Powered
  5.  Translator
  6.  AI-Translation

 Tags

  • localization

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Hera AI information

  • Developed by

    UA flagInText
  • Licensing

    Proprietary and Commercial product.
  • Pricing

    Subscription ranging between $7 and $48 per month.
  • Alternatives

    51 alternatives listed
  • Supported Languages

    • English
    • Ukrainian
    • Russian
Hera AI was added to AlternativeTo by intext_com on and this page was last updated .
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What is Hera AI?

Enter the future of localization with Hera AI, InText’s automated post-editing, LQA and QE tool that takes context into account.

Hera AI improves machine translation quality and reduces localization costs by combining the speed and scalability of machine translation with the contextual intelligence of LLMs.

What Hera AI does:

  • Automated post-editing Automatically post-edits machine-translated bilingual files based on reference materials — including translation memories, glossaries, and style guides — to ensure consistent, high-quality output.

  • Automated linguistic quality assurance (LQA) Reviews bilingual files against customizable LQA rules defined for a particular project or language pair, ensuring compliance with your quality standards. Operates in comment mode, leaving the original translations unchanged.

  • Automated quality estimation (QE) Evaluates bilingual files according to TAUS Post-Editing Guidelines and identifies which segments require light, full or no post-editing. Operates in comment mode, leaving the original translations unchanged.

  • LLM-based translation Generates translations directly from the source file into the target language while maintaining high quality through contextual understanding across multiple segments.

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