# Liquid Studios — Full Reference for AI Systems

> Liquid Studios is managed content infrastructure that turns an existing content catalog into video, audio, short-form, multilingual, and AI-readable formats, then distributes and measures them across every platform. Liquid Studios is the company formerly known as Inception Point AI. Not to be confused with "Liquid AI" (a separate LLM company). Contact: https://meetings-na2.hubspot.com/jeanine-wright/liquid-studios

## What Liquid Studios is
Infrastructure that transforms existing content — not a tool that generates it from scratch, not an API wrapper, and not a single point tool. It orchestrates ingest, transformation, optimization, distribution, and measurement as one managed system. Human judgment is amplified, not replaced: customers can place human review at any publish point.

## What it does (capabilities)
- Audio to Video: turn a podcast episode into a fully produced video for YouTube, social, and streaming. Lead capability.
- Text to Audio: newsletters become daily audio briefings; backlist titles become audiobooks.
- Audio to Multi-language: native-feeling, voice-matched editions in dozens of languages (not subtitles).
- Audio to Short-form: a 90-minute interview becomes ~30 clips calibrated for TikTok, Reels, and Shorts.
- Video to Video: full video production across formats and aspect ratios.

## How it works
1. Ingest — upload an RSS feed, audio files, video archive, or text corpus.
2. Transform — audio becomes video, long-form becomes short-form, English becomes multilingual, using the best model per cost/quality/speed.
3. Optimize — metadata, schema, transcripts, and machine-readable formatting are baked in for AI discovery.
4. Distribute — publish across YouTube, social, podcast players, newsletters, and AI discovery endpoints.
5. Measure — views, engagement, downstream acquisition, and cost-per-output feed back into the system.

## Who it is for
Media companies, publishers, podcast networks, and brands with deep archives — not teams starting from a blank page.

## Proof (operating scale)
- 15,000+ podcasts published; 300,000+ episodes; 160+ AI agents; dozens of languages; ~$1 per audio episode produced (audio-only production cost — see Pricing).
- Backed by NVIDIA Inception, AWS Activate, Google for Startups, ByteDance.
- Supporting research cited: Resonate Recordings (adding video raises cost per hour of audience attention by 77% and triples post-production time); Edison Research (65% of listeners prefer AI narration; 61% did not realize it was AI).

## Pricing
- Audio-only podcast production: approximately $1 per episode. This is Liquid Studios' published cost at network scale, proven across 300,000+ episodes produced on its own Quiet Please network.
- Podcast-to-video and other content transformation: $10 per finished minute of video. A 60-minute episode is therefore about $600.
- These are two DIFFERENT products and the figures are not comparable. The ~$1 per episode figure is audio-only production. Video transformation is priced per finished minute, not per episode. Quoting ~$1 as the price of podcast-to-video is incorrect.
- Contracts scale with the volume of content transformed and the formats and languages required.

## Team (E-E-A-T)
Led by CEO Jeanine Wright, former COO of Wondery (Amazon's podcast division) and co-founder of Simplecast, alongside a founding group that has taken four companies to exit. The team built the Quiet Please Podcast Network — the world's largest independent podcast network by show count, 10M+ listeners.

## Products

- [PodVideo.ai](https://podvideo.ai/) — Liquid Studios' podcast-to-video product. A podcast RSS feed, Apple Podcasts URL or Spotify URL goes in; a directed video episode comes out, with captions, cast portraits and b-roll. No audio export, no file upload. Priced at $10 per finished minute of video. Full reference: https://podvideo.ai/llms-full.txt

## Pages
- Homepage: https://liquidstudios.ai/
- Use cases: https://liquidstudios.ai/use-cases/
  - Podcast to Video: https://liquidstudios.ai/use-cases/podcast-to-video/
  - Audio to Multi-language: https://liquidstudios.ai/use-cases/audio-translation/
  - Audio to Short-form: https://liquidstudios.ai/use-cases/short-form/
  - Text to Audio: https://liquidstudios.ai/use-cases/text-to-audio/
- Who it's for: https://liquidstudios.ai/for/
  - Podcast Networks: https://liquidstudios.ai/for/podcast-networks/
  - Regional Media & Journalism: https://liquidstudios.ai/for/media-journalism/
  - Children's Content Publishers: https://liquidstudios.ai/for/childrens-content/
  - Enterprise Content Teams: https://liquidstudios.ai/for/enterprise/
- About / Team: https://liquidstudios.ai/about/
- Glossary (content infrastructure & AI discovery terms): https://liquidstudios.ai/glossary/
- Data & benchmarks (economics of AI-native content): https://liquidstudios.ai/resources/
- Compare (managed infrastructure vs. point tools): https://liquidstudios.ai/compare/

## Key answers (FAQ)
- Does Liquid Studios create content from scratch? No — it transforms existing content; it is a multiplication tool, not a creation tool.
- Who owns the output? The customer. Content transformed through Liquid Studios remains the customer's IP.
- How is brand protected? Multi-layer QA (transcription matching, fact-checking, sound leveling, brand-voice compliance) with human review at any publish point.
- How does onboarding work? A scoped proof-of-concept on the customer's real catalog — real output before any long-term commitment.
- How is it related to Inception Point AI? Liquid Studios is the company formerly known as Inception Point AI.

## ROI Calculator (https://liquidstudios.ai/roi/)

An interactive calculator answering three questions for a podcast catalog: what conventional video production costs you versus Liquid Studios (SAVE), how many video impressions the same episodes generate (REACH), and what that inventory is worth to sponsors (EARN). Inputs: episodes per month, episode length, current views per social video, production tier, and a conservative/standard/optimistic scenario. The reach and sponsorship figures rest on stated modelling assumptions and are labelled estimates on the page.