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𝗖𝗮𝗿𝗹𝗲𝘀 𝗥𝗲𝗶𝗻𝗮 / 𝗘𝗹𝗲𝘃𝗲𝗻𝗟𝗮𝗯𝘀 𝗚𝗧𝗠 + 𝗦𝗮𝗹𝗲𝘀 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 🔥 ElevenLabs hit ~$330M ARR in ~3 years with a world class revenue system That system is lead by Carles Reina, ElevenLabs’ first GTM hire + early investor (as well as early Revolut investor & founder of Baobab Ventures). His GTM philosophy is brutally simple: - High-output per head - Small teams - Aggressive productivity - A revenue engine where expansion becomes repeatable We distilled the Carles Reina / ElevenLabs GTM + Sales Org playbook into a copy-paste operating system any CEO can deploy, whether you’re at $0, $1M, or $20M ARR. It includes: 1) The 6 primitives every CEO must understand 2) The ElevenLabs philosophy: Quota multiples as a culture filter + pessimistic forecasting to force real pipe. 3) System design before comp: How to segment SMB / Mid-Market / Enterprise + choose the right revenue unit (New ARR vs ACV vs TCV). 4) Copy-paste comp plans: SDR, AE, CSM, Managers, SE/SC 5) The most valuable move: the ElevenLabs expansion model 6) Operating cadence: Monthly pipeline review, weekly outbound discipline, performance management. Comment “ELEVENLABS” below and I’ll send you the link 👇

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Comment “ELEVENLABS” below and I’ll send you the link 👇

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I've reviewed a lot of voice stacks this year. The failure point is almost always the same.   𝗧𝗵𝗲 𝗛𝗮𝗻𝗱𝗼𝗳𝗳 𝗣𝗿𝗼𝗯𝗹𝗲𝗺   Every production voice stack has the same invisible failure point.   Transcription reads the words. Speech model receives a text string. Nothing else crosses that gap.   Not the pitch. Not the pace. Not the fact that the caller's sentences just got shorter because they're about to lose it.   By the time the speech model generates a response — the emotional context is gone. It never arrived.   Your agent sounds flat because it's working with incomplete data. Every time.   𝗪𝗵𝗮𝘁 𝗘𝗹𝗲𝘃𝗲𝗻𝗟𝗮𝗯𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗕𝘂𝗶𝗹𝘁   They closed that gap by owning both sides of it.   → 𝗦𝗰𝗿𝗶𝗯𝗲 𝘃𝟮 𝗥𝗲𝗮𝗹𝘁𝗶𝗺𝗲 doesn't just transcribe. It reads prosody as the caller speaks and passes emotional signal directly into the speech layer. Same stack. No translation. No data loss.   → ElevenLabs 𝘃𝟯 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 receives that signal and maintains context across the full conversation — not just the last turn. It knows the caller was frustrated three exchanges ago. That shapes delivery now.   → 𝗧𝘂𝗿𝗻 𝗘𝗮𝗴𝗲𝗿𝗻𝗲𝘀𝘀 inside ElevenAgents gives you a second lever: ↳ Patient mode — holds space. Built for complaints, collections, crisis ↳ Eager mode — fast responses. Built for scheduling, lookups, confirmations   𝗪𝗵𝘆 𝗡𝗼 𝗢𝗻𝗲 𝗖𝗮𝗻 𝗖𝗼𝗽𝘆 𝗧𝗵𝗶𝘀   You can't replicate this by upgrading your TTS vendor.   The signal path between transcription and speech has to be native. The moment you introduce a third-party boundary — even a good one — the emotional data doesn't cross. You're back to text strings.   Sierra. Decagon. Vapi. Most use ElevenLabs as a provider. ↳ They're downstream of the stack — not owners of it.   That gap is not closeable from the outside.   I ran the Airlines demo. Frustrated passenger. Rebooking under pressure. Sentences getting shorter, pace spiking.   The agent caught it early — slowed its own delivery before the caller even finished the sentence.   That's Scribe v2 feeding v3 Conversational in real time. ↳ Link to the demo is in the first comment.   Post 3 is the full build — system prompts, explicit tags, turn eagerness by use case. Drop 𝗔𝗥𝗖𝗛 in the comments and I'll tag you when it drops.

Drop 𝗔𝗥𝗖𝗛 in the comments and I'll tag you when it drops.

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