AI-enabled full-stack software
Rakshith manages the delivery of an AI-enabled post-production platform for podcast studios, now in a live pilot. The platform has a web application and a Premiere Pro integration.
Post-production time per episode
Studio team, by hand
Supervised output, in pilot
Video and clips ready when the recording stops
What
Rakshith owns the delivery of the platform through its pilot phase. The work covers sprint execution, pilot onboarding and the communication between the two software engineers and senior management. The product brings several AI models into one editing pipeline.
How
Rakshith uses a structured delivery method. Sprints, epics and feature sets are planned and tracked in JIRA against the project timeline. Documents and decisions are recorded in Confluence. User workflows are mapped in Miro, so the editing pipeline is specified from the studio's point of view before development starts.
Each pilot studio goes through a full onboarding process. A discovery call confirms that the studio's workflow is a good fit. A structured onboarding follows. Regular pilot reviews record problems, which go directly into the sprint backlog.
Results
Two podcast studios use the platform every week. The pilot stays at two studios while the platform matures. Post-production time for a pilot episode decreased from four days of manual editing to four hours of supervised output. The engineering target is four minutes.