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The exact AI playbook (MCPs, GPTs, Granola) that saved ElevenLabs $100k+ & helps them ship daily

Luke Harries, Head of Growth at ElevenLabs, the leading AI voice technology company, shares how he’s automating marketing workflows with AI—from case studies to translations to WhatsApp integrations—saving his company over $140,000 while making everything a launch. *What you’ll learn:* 1. How to create polished case studies in minutes using AI transcription and a custom GPT 2. How ElevenLabs built a custom AI translation system that saved them $140,000 annually and eliminated agency headaches 3. How to use Model Context Protocols (MCPs) to connect AI assistants to your WhatsApp messages 4. The “everything is a launch” philosophy that helps ElevenLabs maintain consistent marketing momentum 5. Why marketers should learn to code with AI tools like Cursor 6. How to create effective custom GPTs by focusing on prompt engineering rather than output editing *Brought to you by:* Orkes—The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/ Retool—AI that’s designed for developers, and built for the enterprise: https://retool.com/howiai *Where to find Luke Harries:* Website: https://harries.co/ LinkedIn: https://www.linkedin.com/in/luke-harries/ GitHub: https://github.com/lharries X: https://x.com/lukeharries *Where to find Claire Vo:* ChatPRD: https://www.chatprd.ai/ Website: https://clairevo.com/ LinkedIn: https://www.linkedin.com/in/clairevo/ X: https://x.com/clairevo *In this episode, we cover:* (00:00) Intro (02:41) The future of AI in marketing (04:22) Using Granola and custom GPTs to write case studies (12:10) Generating tweet threads using ChatGPT (13:58) Building case studies into a systematic workflow (15:14) Best practices for prompt engineering (19:39) Building a custom translation system that saved $140k (25:10) Open sourcing the translation solution (29:47) Building a WhatsApp MCP (38:07) Creating specialized AI agents on demand (41:08) Lightning round and final thoughts *Tools referenced:* • Granola: https://www.granola.ai/ • ChatGPT: https://chat.openai.com/ • Cursor: https://www.cursor.com/ • Claude: https://claude.ai/ • ElevenLabs: https://elevenlabs.io/ • WhatsApp: https://www.whatsapp.com/ • GitHub: https://github.com/ • Zapier: https://zapier.com/ • Calendly: https://calendly.com/ • Salesforce: https://www.salesforce.com/ *Other references:* • MCP (Model Context Protocol): https://www.anthropic.com/news/model-context-protocol • WhatsApp MCP repo: https://github.com/lharries/whatsapp-mcp • Whatsmeow library: https://github.com/tulir/whatsmeow • LaunchDarkly: https://launchdarkly.com/ • Introducing ElevenLabs MCP: https://elevenlabs.io/blog/introducing-elevenlabs-mcp • Ordering a pizza using the ElevenLabs MCP server: https://x.com/elevenlabsio/status/1909300782673101265 • Chess.com: https://www.chess.com/ • Lovable: https://lovable.ai/ • v0: https://v0.dev/ • Figma: https://www.figma.com/ • Launch and launch again — how to launch your products: https://harries.co/launch-your-product • Your first growth hire: https://harries.co/first-growth-hire _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email jordan@penname.co._

Luke HarriesguestClaire Vohost
Jun 2, 202544mWatch on YouTube ↗

CHAPTERS

  1. Prompt-first editing mindset + episode promise: “vibe marketing” that ships daily

    The episode opens with Luke’s core principle: improve systems by editing the underlying prompt/workflow, not manually tweaking outputs. Claire frames the conversation as “vibe marketing”—using AI to turn everyday work into repeatable launch assets and real cost savings at ElevenLabs.

  2. The AI CMO: automate launches from feature → messaging → distribution

    Luke describes why marketing becomes the bottleneck as software creation accelerates. He outlines an AI-powered launch checklist that converts new features into value props, messaging, assets, and ongoing channel experimentation (ads, landing pages, optimization).

  3. Live case study workflow: record a customer interview with Granola

    Luke demonstrates a lightweight case-study interview using Granola to capture transcript and notes. The point is to reduce friction: short interviews produce structured inputs that can be immediately turned into publishable marketing content.

  4. Custom GPT “Copy Editor”: enforce brand voice with rules + examples

    Luke walks through an internal custom GPT used across the company to match ElevenLabs’ tone and style. It includes explicit role definition, strict must-do instructions, voice/tone guidelines, and reference examples (tweets and blog posts).

  5. From notes → publishable case study: use summary + raw transcript for fidelity

    Luke shows how he pastes both Granola’s summary and the raw transcript into the custom GPT to generate a case study draft that’s usually usable on the first pass. Including the raw transcript preserves exact quotes and prevents “lossy” summarization issues.

  6. Turn the case study into distribution: tweet threads with asset placeholders

    Luke demonstrates generating an X (Twitter) thread from the same source content, including guidance on what visuals to include. Claire highlights how this accelerates distribution by producing not just copy but a content packaging plan.

  7. Make it a system: Zapier + CRM triggers to produce case studies continuously

    They zoom out from one-off creation to operationalizing it as a repeatable engine. Luke proposes connecting Salesforce closed-won events to automated outreach and scheduling, so the team consistently generates multiple case studies per month.

  8. Prompt engineering best practices: tighten instructions and feed learnings back

    Claire and Luke dissect why the prompting approach works: clear role, specific requirements, formatting constraints, content-type sections, and examples. Luke reinforces the practice of updating prompts whenever recurring issues appear rather than editing final text repeatedly.

  9. Replacing localization SaaS + agencies with an LLM translation system ($140k+ saved)

    Luke details how ElevenLabs scrapped an expensive localization tool and agency-based translation pipeline due to cost, delays, and poor quality. He built a small server and prompt-per-language approach—mostly in Cursor—that delivers instant translations and reduced vendor overhead.

  10. Why build beats buy (sometimes): SaaS risks and ‘human-in-the-loop’ exposure

    Claire and Luke discuss broader implications: when build cost drops, teams will increasingly replace niche SaaS—especially where value is routing low-skill human labor. Luke argues SaaS isn’t dead, but products dependent on manual labor arbitrage are vulnerable as AI improves.

  11. Open-sourcing the translation approach: GitHub Actions + CMS ‘Translate’ button + Cursor rules

    Luke explains the concrete implementation and why it worked better: prompt control, one source of truth, and automation at the code/CI layer. The system translates strings on each push and adds a CMS translation button, plus a Cursor rule to extract strings into JSON cleanly.

  12. MCP explained via WhatsApp: give AI agents tool access to your messages

    Luke introduces Model Context Protocol (Anthropic) and why it matters: it lets agents use external tools and data sources. He built an unofficial WhatsApp MCP to download messages locally into SQLite and let Claude query/summarize them or send messages/voice notes.

  13. From rigid automations to flexible agent workflows: stitching MCPs together

    They contrast static automation (Zapier/n8n) with chat-driven, tool-using agents that can adapt to new tasks on the fly. Luke shows combining multiple MCPs (WhatsApp + ElevenLabs) to create workflows like summaries and even voice roundups, pointing toward “tabs going away.”

  14. On-demand specialized agents + lightning round: voice unlocks new products and ops

    Luke demonstrates creating specialized agents on the fly (e.g., a case-study interview agent; mentions pizza-ordering demos). In the closing lightning round, he argues voice enables new customer experiences (tutors/coaches) and scalable back-office functions like multilingual support.

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