Podvynt was built by a podcast network audio lead who spent years running production across 20+ shows with disconnected tools. The mastering pipeline couldn't see the retention curves. The analytics couldn't see the audio. We built the bridge.
In 2023, Yuki Tanaka was the senior audio production lead for a 22-show podcast network based in Austin. The shows were good — real editorial quality, growing audiences. The infrastructure holding them together was not. Every Monday was a variant of the same problem: six different hosts recording in six different rooms with six different mic setups, all landing in the edit queue with wildly inconsistent levels. Normalizing them took 3–4 hours per episode, per engineer. Filler word removal was done by hand. And when it came time to pull a social clip, producers guessed — sometimes right, mostly not.
Meanwhile, the analytics were split across Spotify for Podcasters, Apple Podcasts Connect, and a Libsyn dashboard that hadn't meaningfully changed its UI since 2018. There was no way to see how a show was performing across all three in one view. There was definitely no way to know if audio quality was affecting listener retention.
Yuki spent six months trying to stitch together a solution from existing tools. Nothing came close. The mastering engineer couldn't see which episodes retained listeners. The analytics dashboard couldn't tell you whether an audio quality drop-off was driving listener exits at the 8-minute mark. The tools were built for different jobs and didn't talk to each other.
Podvynt was founded in early 2024 to close that loop. We launched with three network partners who helped shape the initial feature set. We're bootstrapped, independently funded, and building for the teams who don't have a dedicated engineering department but still need production infrastructure that works at network scale.
Former senior audio production lead for a 22-show podcast network in Austin. Spent three years solving the same post-production and analytics problems before building Podvynt to fix them at the infrastructure level.
Previously led ML infrastructure at a major audio streaming platform. Specialist in audio DSP, speech processing, and distributed media pipelines.
Former podcast producer at a 12-show network. Brings real production experience into every product decision — if a feature doesn't save time in a real newsroom, it doesn't ship.
Every feature we build has to return measurable time to producers. We track hours-per-episode across our network customers. If a feature doesn't move that number, it doesn't ship.
Clip recommendations, churn scoring, production quality benchmarks — all validated against real listener behavior. When the data contradicts our assumptions, we update the assumptions.
Our founding customers shaped the initial feature set. We run quarterly co-design sessions with Network-tier customers. If you run a podcast network, you have a direct line to our product roadmap.
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