Inkitt reduces video production time by 70% with custom AI harness
Inkitt launched a custom AI harness that generates short-form video trailers, reducing production time from weeks to minutes and cutting costs by 70%. This shift towards in-house AI solutions allows โฆ
Inkitt, the digitalโpublishing platform known for dataโdriven author discovery, unveiled a custom AI harness that creates shortโform video trailers for its catalog, rolling it out to users in June 2024. The company says the tool cuts production time from weeks to minutes and lets it experiment with AIโgenerated visuals without relying on external providers.
Enterprises are turning to AI harnesses because the race to build the biggest language model has slowed, and the real value now lies in how quickly a business can apply AI to its own workflow. A harness bundles a model, data pipelines, and user interfaces into a single, reusable component. It gives firms control over proprietary data, reduces dependence on costly API calls, and aligns AI output with brand standards. Inkittโs move follows a wave of similar projects at media firms, retailers and insurers that need to embed AI while meeting privacy and compliance rules.
Inkitt highlighted five lessons from its experiment. First, owning the data pipeline lets the company protect author manuscripts and reader analytics. Second, the custom harness lowered perโvideo costs by about 70โฏ% compared with thirdโparty services. Third, the team built a simple UI that lets editors generate and edit videos without coding, speeding timeโtoโmarket. Fourth, the harness integrates with Inkittโs existing recommendation engine, creating a seamless flow from story discovery to visual promotion. Fifth, the project required a small, crossโfunctional team, showing that a dedicated AI squad can deliver results without a massive hiring spree.
Analysts say Inkittโs success could spur more firms to develop inโhouse harnesses rather than buying generic APIs. The next step for Inkitt is to expand the tool to longerโform content and to open an internal marketplace where other departments can plug in their own models. If the approach scales, it may reshape how companies budget for AI, shifting spend from licensing fees to building reusable, companyโspecific AI infrastructure.
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