The Impact
40
%
faster copy-cleaning cycles across desks
85
%+
acceptance rate on AI-suggested corrections
50
%
reduction in late-stage editorial fixes
Unframe crosses $100M TCV in under 12 months and raises another round 🚀
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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.
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.
