The Challenge
Across fast-paced newsrooms, editors were spending excessive time on manual copy cleanup - chasing style inconsistencies, verifying foreign name spellings, and correcting avoidable language errors. With stories filing continuously throughout the day, many of these issues slipped past first reads and accumulated later in the production cycle. Early factual mistakes - such as misnamed places or inconsistent references - were still being caught too far downstream, creating bottlenecks and slowing the publishing pipeline. Teams needed a reliable, on-brand editorial assistant that could reduce friction without lowering editorial standards.
The reason we wanted to work with Unframe was to improve our quality and take our proofreading team’s capabilities to the next level with AI.
The Solution
Unframe introduced an AI proofreading assistant trained on the outlet’s stylebook, transcription rules, and editorial patterns. It scans copy for style slips, language misses, and early fact issues, surfacing clear, in-context suggestions that editors can accept or override in seconds. The tool handles everything from foreign-name transcription to tricky punctuation and location checks, giving editors a faster, cleaner first pass. Throughout the workflow, every decision feeds a learning loop - making the assistant sharper with each article.
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