I Built an AI System That Automates My Proposals (n8n + Gamma)

I Built an AI System That Automates My Proposals (n8n + Gamma)

🎙 Nate Herk 👥 964K 📅 January 19, 2026 ⏱ 20 min 👁 57K 📄 tutorial 🧭 2026-08-28
Available in: English (current) Français

Keywords

n8nGammaAPIautomationproposal

Summary

The video presents a two-part n8n workflow that automates the creation of client proposals from meeting recordings. The first workflow logs meeting details into a Google Sheet via a webhook from Fireflies, including a polling mechanism to wait for AI-generated summaries. The second workflow, triggered by new rows, retrieves the meeting info, cleans the transcript, and asks for human approval via Slack. Upon approval, a proposal agent generates a structured proposal, which is then sent to Gamma’s API to create a professional slide deck. The system includes error handling, input standardization, and automatic sharing of the final deck. The creator demonstrates the entire process with a live run, showing the generated deck and discussing customization options. He emphasizes that the system gets you 90% of the way, requiring human review before sending to clients. The video also covers how to set up the Gamma API call, including parameters like text mode, theme ID, and sharing settings. Finally, he explains how to standardize inputs for scalability and offers a free download of the workflow.

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Critical Evaluation

Value of the Information & Strength of the Argument

The video provides high practical value for professionals interested in AI automation, offering a concrete, step-by-step implementation of a real-world use case. The argumentation is solid, based on the creator’s direct experience, with clear explanations of each node and the reasoning behind design choices (e.g., splitting workflows, polling, standardizing inputs). The demonstration of the live run and the final output adds credibility. However, the video is essentially a tutorial and does not engage with alternative approaches or potential limitations in depth.

Scientific Rigor, Source Quality, Title Accuracy

The video is a tutorial, and the scientific rigor is moderate. The creator does not cite external sources, but he references Gamma’s API documentation and shows how to use it. The title accurately reflects the content. The description includes links to his own courses and tools, which are promotional rather than scientific. No comments were provided for analysis.

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Title / Content Match

The title accurately reflects the content: a step-by-step build of an AI system for automating proposals using n8n and Gamma.

Quality & Reliability

7/10

The video is a practical tutorial demonstrating a specific AI automation workflow. It provides detailed technical explanations and shows real outputs, but relies on personal experience and does not cite external scientific sources. The approach is reproducible and transparent, but lacks formal validation.

Chapters

Cited Sources

Concurring Sources

  • n8n documentation — Official documentation for n8n, which aligns with the workflow described.
  • Gamma API documentation — Official API documentation for Gamma, which the video references for building the API call.

Contribution & Novelties

The video offers a practical, end-to-end example of integrating n8n with Gamma’s API to automate a common business task. It provides insights into handling API webhooks, polling for AI-generated data, and standardizing inputs for scalability. The approach is not entirely novel but serves as a useful template for similar automations.

Pour aller plus loin :

  • n8n documentation — Official documentation for n8n, useful for understanding nodes and workflows.
  • Gamma API documentation — Official API documentation for Gamma, detailing endpoints and parameters.
  • Fireflies.ai API — API documentation for Fireflies, relevant for meeting transcription and webhooks.
  • Claude AI — AI assistant used for writing code nodes, as mentioned in the video.

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Radar Profile

The radar profile shows high scores in quantity of information and technical level, reflecting the detailed tutorial nature. Quality and reliability are slightly lower due to the lack of external validation and reliance on personal experience. Overall, it's a solid practical resource.

Reliability 7/10