Pre-SeedBacked by Concept Ventures

Eleven Labs

Building Voice AI

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Eleven Labs

Description: Building Voice AI

Investors: Concept Ventures

Reference Link to Deck: https://drive.google.com/file/d/16p8InLz7fl4OV2LXHKbLSGlVP7X34uUm/view

Stage: Pre-Seed

Slide 1 — Title

  • Text: “Eleven — Powering content in any language with automatic dubbing”

  • Slide 2 — Introduction

  • Text: “People want to listen to and watch content in their native language”
  • Dubbing = swapping original audio with new voice recordings
  • Expensive: ~$100/min (actors, studio, post-production)
  • Slow: >2 weeks for a 10-minute video, longer projects can take months

  • Slide 3 — Problem

  • Text: “There are no affordable tools to make content watchable in any language with high quality.”

  • Slide 4 — Solution

  • Text: “Human-quality automated dubbing as a SaaS”
  • Human Quality: preserves voice features, emotions, intonation
  • Personalized: your own voice across languages
  • Simple & Quick: SaaS, one-click dubbing, optional human-in-the-loop

  • Slide 5 — Solution Prototype Deep-dive

  • Steps:
  • 1. Input movie/audio (English)

    2. Subtitles via ASR/metadata

    3. Translation A → B

    4. Background noise + dialogue separation

    5. Automatic dubbing (core tech)

    6. Dubbed video output

  • Demo: 10-minute video dubbed in ~2 minutes

  • Slide 6 — Team

  • “We have studied, lived and worked together. Best friends since high school.”
  • Piotr Dabkowski | CTO

  • Ex-Google ML researcher
  • Oxford & Cambridge CS
  • NeurIPS papers (300+ citations)
  • Open-source (Js2Py, 250k monthly downloads)
  • Mati Staniszewski | CEO

  • Deployment Strategist @ Palantir
  • Math @ Imperial College London
  • BlackRock & Opera experience
  • Founded MathScon (1,000+ attendees)

  • Slide 7 — Vision

  • Text: “Eleven’s automatic dubbing will power seamless communication and content across any language.”
  • Expansion use cases: real-time dubbing, voice conversion, professional dubbing, ads, games, podcasts, creators
  • Logos shown: Meta, AT&T, Zoom, Netflix, Disney, Marvel, Lionbridge, Omnicom, Audible, Activision Blizzard, Apple Podcasts, YouTube, Twitch, Final Cut

  • Slide 8 — Expanding Total Available Market

  • $2B → pro creators (podcasts, video)
  • $4.6B → game localization + movie dubbing
  • $24B → localization, translation, interpreting

  • Slide 9 — Market Size Deep-dive

  • 50M+ creators globally (TAM)
  • 2M professional creators (SAM)
  • 100K YouTubers w/ >500K subs (SOM)
  • 10K caption-uploading creators (Immediate Market)
  • Content: 3 videos/mo × 10 min × 3 languages = 9M minutes/mo
  • Revenue: ~$1/minute ≈ $110M/year

  • Slide 10 — Case Study: MrBeast

  • English channel: 96M subs (since 2012)
  • Spanish channel: 19M subs (since 2021, pro-dubbed)
  • One dubbed video = ~$50K revenue
  • Insights: dubbing multiplies reach + revenue, quick turnaround needed, data volume = defensibility

  • Slide 11 — Traction & Feedback

  • Content redacted

  • Slide 12 — Competition

  • Competitive quadrant: Quality vs Speed
  • Top-right: Eleven (high quality + fast)
  • Top-left: Traditional dubbing studios (Sonic Union, IMIN Creative)
  • Bottom-left: Semi-automated (Deepdub.ai, Papercup)
  • Bottom-right: TTS tools (Amazon Polly, IBM Watson, Google Wavenet)

  • Slide 13 — Competitive Advantage (Research)

  • “New way to automatically dub — preserves speaker’s voice, emotion, intonation”
  • Not TTS — uses both speech + text inputs
  • Novel speech representation:
  • Prosody (emotions, intonation)
  • Speaker embeddings (voice identity)
  • Fast, affordable, scalable to new languages
  • Diagram: Video → Speech → (Prosody + Captions/ASR + Speaker’s voice) → Translation → Prosody mapping → Dubbing generation

  • Slide 14 — Closing

  • Text: “Eleven — Powering content in any language with automatic dubbing”

  • Walkthrough

    The ElevenLabs deck begins with a bold claim: “Powering content in any language with automatic dubbing.”

    From the start, the founders highlight the pain points of traditional dubbing: it’s costly (~$100/minute), painfully slow (weeks for just a short video), and inaccessible to most creators.

    Their solution is an automated SaaS platform that delivers human-quality dubbing while preserving voice identity, emotional tone, and intonation. Users can even have content dubbed into multiple languages using their own voice. A prototype demo shows a 10-minute video processed in just two minutes — a dramatic leap from industry norms.

    The team emphasizes their credibility: lifelong friends Piotr (ex-Google ML researcher, NeurIPS author, open-source developer) and Mati (Palantir strategist, math background, conference founder).

    Eleven’s vision stretches far beyond YouTubers — real-time dubbing for video calls, privacy-friendly voice conversion, professional movie dubbing, ads, games, podcasts. Logos of Netflix, Disney, YouTube, and others underline the scope.

    The market is vast: $2B for creators, $4.6B for gaming and film dubbing, $24B for localization overall. Eleven narrows its immediate focus on the 100K YouTubers with >500K subs, which they peg as a $110M+ opportunity.

    A case study of MrBeast proves the point: his Spanish-dubbed channel (launched in 2021) already has 19M subs, with single videos generating ~$50K. The message: dubbing can multiply reach and revenue, and creators will follow.

    Traction data is redacted, but the competitive landscape is clear. Traditional studios are too slow, semi-automated players are too manual, and TTS systems lack nuance. Eleven positions itself as the only option that is both high-quality and fast.

    Their technical moat: a novel dubbing system that combines text, speech, and prosody with voice embeddings to produce lifelike multilingual speech. This architecture allows scalability to new languages in minutes while preserving identity and authenticity.

    The deck ends as it began: Eleven wants to become the backbone for making global content seamless — human quality dubbing, delivered in minutes, at scale.

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