Seekho partners with ButterCut to power content operations at scale
Seekho reaches millions of learners across India. Multiple topics, multiple languages, and a content team that has to keep up with all of it. We're thrilled to partner with them — building AI workflows behind their content operations so production stays fast and quality holds as they grow.
The problem most teams don't name
Most content businesses hit the same wall at scale. It's not a talent problem or a budget problem. It's that the production workflow was built for a smaller operation and nobody rebuilt it when the volume grew. Manual processes that worked at 50 videos a month quietly break at 500.
Subtitle accuracy from manual editors typically runs at 80 to 90 percent. That's fine for a lot of content. For an edutainment platform where learners follow along word for word, that gap shows up fast. And when something's wrong, the fix goes back to the original editor. You're waiting 1 to 2 days per video. At any real volume, that becomes a delivery problem nobody budgeted for.
Most teams don't realise their content ops have a ceiling until they're already hitting it.
What ButterCut actually does
We build custom AI content workflows for teams producing video at scale. Not a plug-and-play tool.
We come in, understand how the team works, what content they produce, what markets they serve, and build around that. The output is a pipeline that runs faster, holds quality consistently, and doesn't require the team to grow headcount every time output grows.
For Seekho specifically: accuracy went to 98%+, corrections happen in real time inside the interface, and the review team spends their time on what actually needs attention — not reading through thousands of lines that are already correct.
The broader point is that every content operation has a version of this problem. Generic tools weren't built for Indian language complexity. Manual review doesn't scale past a certain point. And the cost of fixing bad content after its published compounds quickly.
What we build is the infrastructure layer between raw footage and finished, quality-checked content. Configurable, accurate, and built around how each team actually operates.