Turn Any Podcast Or Meeting Transcript Into A Blog Post With Make.com

Podcasters and content teams sit on a goldmine they rarely fully exploit. A 45-minute podcast episode contains 6,000–8,000 words of expert insight — enough for two or three high-quality blog posts. But manually transforming a transcript into a readable, SEO-optimized article takes a skilled writer 2–4 hours: cleaning filler words, restructuring the conversational flow into logical sections, adding context, and formatting for web. Most teams simply don’t have the bandwidth, so the content sits unused.

This Make.com workflow automates the entire pipeline. The moment Rev.com delivers a finished transcript, the automation fires: it cleans the raw text, sends it to GPT-4o with your target keyword and brand tone instructions, and produces a structured draft blog post inside Google Docs within about 90 seconds. The two-step GPT-4o approach — outline first, then prose — is the key design decision here. It prevents the “rambling wall of text” problem common in single-shot transcript conversions and produces drafts that need only light editorial polish rather than full rewrites.

The $0.05-per-post estimate assumes a 45-minute podcast transcript (~6,500 words in, ~1,200 words out) processed through GPT-4o. Your human editor still needs 15–20 minutes to review, add internal links, and approve — but the 3–4 hours of drafting work is gone.

Prerequisites

1. A Make.com account (Free tier supports this workflow; Starter plan at $9/month recommended if processing more than 3 transcripts/week due to operation limits)
2. An OpenAI API key with GPT-4o access
3. A Rev.com account with webhook notifications enabled (Settings → Integrations → Webhooks)
4. A Google Drive folder named “Blog Drafts” (or your preferred name — you’ll configure the folder ID in setup)
5. A Notion database for your editorial calendar with properties: Title (text), Status (select), Google Doc URL (URL), Rev Order ID (text), Created Date (date)
6. Your target keyword list ready — these are manually entered per blog post in a Notion “Keyword” property that the workflow reads

Setup Guide

1. Import the Make.com blueprint — Download the JSON file and go to Make.com → Create a new scenario → Import Blueprint. Upload the file.
2. Set up the Rev.com webhook — In Make.com, copy the generated webhook URL from the Webhooks module. Paste it in Rev.com under Settings → Notifications → Webhook URL. Select “Transcription Complete” as the trigger event.
3. Configure the Text Parser — The default regex pattern strips standard Rev.com speaker labels (SPEAKER_NAME:\s). If you use a custom Rev.com template, update the regex to match your speaker label format.
4. Add your OpenAI API key — In both OpenAI modules, select “Create a connection” and paste your API key. Set model to gpt-4o, max tokens to 2000 for the outline step and 4000 for the full post step.
5. Customize the system prompt variables — In the prompt, set your default [AUDIENCE], [TONE], and [POST_LENGTH] values. Leave [TARGET_KEYWORD] as a dynamic value pulled from the Notion database by Rev order ID lookup (the blueprint includes this lookup step).
6. Link Google Drive — Authenticate the Google Docs module and select your “Blog Drafts” folder. The filename will be auto-set to the GPT-4o-generated H1 title.
7. Link Notion — Authenticate the Notion module and select your editorial calendar database. Map the fields as described in the blueprint.
8. Run a test — Upload a short test transcript to Rev.com (even a 5-minute clip works) and verify the full pipeline produces a Google Doc within 2 minutes.

Who This Is NOT For

• Teams that need verbatim transcription accuracy for legal or compliance purposes — GPT-4o paraphrases and restructures; it is not a transcription tool
• Highly technical content (medical, legal, engineering) where factual accuracy is critical and every claim needs citation — human expert review is non-negotiable for those domains even after this automation
• Teams whose podcasts are interview-heavy with 3+ speakers and complex crosstalk — Rev.com speaker labels become unreliable, and the blog post attribution gets messy
• Anyone not using Rev.com for transcription — this blueprint uses Rev.com’s webhook format specifically; Otter.ai, Descript, or Riverside users will need to modify the trigger step