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3d ago · 8 min read · Your transcription workflow probably touches 5 or 6 tools per job. Every transition between those tools costs time you are not measuring — and that time compounds across every job you process. Count the tools in your current transcription workflow. ...
Join discussion3d ago · 7 min read · AI solved the typing. Formatting is still your problem. Here is why — and what a different workflow looks like. If you work in transcription in 2026, you have probably noticed something that does not get discussed enough: the accuracy problem largel...
Join discussion3d ago · 8 min read · Timestamp problems are the most avoidable category of manual work in transcription. Most workflows absorb this cost silently every week. Timestamp errors are not interesting. They do not require judgment or expertise. They do not involve nuanced dec...
Join discussion3d ago · 8 min read · High-volume agencies are not just faster. They have eliminated entire categories of manual work. Here is what that infrastructure looks like — and where most agencies lose the most time. An agency processing 10 hours of audio per week and an agency ...
Join discussion3d ago · 9 min read · The benchmark accuracy numbers for AI speaker diarization are deceptively good. Here is what actually happens on production audio — and why these problems persist despite years of improvement. Speaker diarization — the problem of figuring out "who s...
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