Case Studies | SaaS B2B

Automating Tagging for Enhanced Translation and Voiceover Accuracy

voxANN
About

voxANN builds tools for the language services industry, helping translation agencies and multilingual voiceover studios synchronise scripts, generate performance directives, and keep translations culturally accurate.

Challenge
  • Tagging words, phrases and subsections in transcripts was done manually, with no systematic structure, slow and prone to inconsistency.
  • Left unresolved, that risked project delays, higher operational costs, and client dissatisfaction across voxANN's translation and voiceover customers.
  • Manual tagging also limited how much voxANN could lean on its own AI technology to scale the platform.
Solution

Cloud Combinator trained a custom classification model in AWS Comprehend on a dataset built jointly with voxANN, automatically tagging words, phrases and subsections via API instead of by hand.

By the numbers:

  • 75 hours of developer up-skilling and build time saved
  • Manual tagging replaced by automated, consistent classification
  • Platform able to handle more simultaneous projects with faster turnaround

What changed:

  • AWS Lambda handled the processing workflow serverlessly, and entity detection was added to tag names, companies, amounts, dates and addresses for extra accuracy.
  • The team received full training on the new system as part of Cloud Combinator's delivery process, covering operation and troubleshooting.
  • voxANN's own developers saved an estimated 75 hours of Comprehend up-skilling time, freeing them to focus on their core prototype.
voxANN

Cloud Combinator proposed a solution using Amazon Comprehend and a few weeks later we had a demo, proving we were on the right track. Integration was a breeze.

Robin Tong

voxANN

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