Accuracy
Whether meaning is preserved.
A focused native Polish audit of AI-translated content to identify accuracy, terminology, fluency and consistency risks before choosing a larger review workflow.
Sample review · recurring patterns · risk · next-step recommendation
Not necessarily. A representative sample can show whether problems are isolated or systematic and whether the content needs targeted correction, broader QA, MTPE, retranslation or no substantial linguistic intervention.
Whether meaning is preserved.
Missing or invented information.
Correctness and consistency of important terms.
Fluency and native readability.
Target-language correctness.
Recurring decisions across the sample.
Fit for audience and purpose.
Whether wording works in its intended use.
AI output can look polished enough to escape casual review. The audit specifically looks for issues that fluent wording can hide.
The audit can provide examples, recurring issue patterns, risk areas and a practical recommendation for the next linguistic workflow. The goal is useful diagnosis, not the longest possible list of edits.
Possible outcomes include targeted corrections, AI Review, MTPE, Linguistic QA, partial retranslation, full human translation — or confirmation that no substantial intervention is needed.
For large content sets, testing a representative sample first can prevent unnecessary editing and help define realistic quality requirements before committing to a larger workflow.
Provide representative content and source where available.
Explain where the Polish will be used and what quality is required.
Assess the sample against relevant linguistic criteria.
Choose the most appropriate next step based on evidence.
Tell me what you are working with and what you need the Polish content to achieve.