This article provides a complete, practical walkthrough of the Postgres Webhook Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Supercharge Your Meetings with Real-Time AI Transcriptions Using n8n Workflow
   
   Meta Description:
   Learn how to automate real-time meeting transcription, keyword tracking, and AI analysis using n8n, Recall.ai, AssemblyAI, Supabase, and OpenAI in this powerful low-code workflow by 5minAI.
   
   Keywords:
   real-time transcription, n8n workflow, Recall.ai, AssemblyAI, OpenAI Assistant, Supabase, meeting automation, AI meeting notes, Google Meet automation, Zoom bot integration, live transcription, AI summaries, automation for productivity
   
   —
   
   Article:
   
   Automating Real-Time Transcriptions and Insights from Meetings with n8n and AI
   
   In a world where meetings define decisions and collaboration, the need to retain and refer back to discussions has never been greater. Manual note-taking and transcription are not only time-consuming but also prone to error and inconsistency. Enter this brilliant n8n-powered automation workflow developed by Mark Shcherbakov from the 5minAI community — a low-code automation setup that leverages multiple AI services to transcribe, analyze, and summarize meetings in real time.
   
   This fully automated system can join virtual meetings, transcribe speech live, analyze dialogue using OpenAI's powerful assistant API, and even extract actionable notes — all without human intervention.
   
   Let’s dive into how this workflow works and the technologies it employs.
   
   The Core Functionality
   
   This n8n workflow acts as an AI-powered assistant that participates in virtual meetings (such as Google Meet and Zoom), transcribes conversations live, performs keyword analysis, and stores results in a structured PostgreSQL database via Supabase. Here’s a breakdown of what it does:
   
   1. Auto-Joins Meetings:
   Using the Recall.ai API, a bot automatically joins your meeting via the provided URL and begins transcribing speech in real-time. You only need to provide the meeting link (which can come from services like Zoom, Google Meet, or Microsoft Teams).
   
   2. Transcribes Speech Live:
   The transcription is handled by AssemblyAI (via Recall.ai’s integration), which breaks down spoken words into structured JSON containing speaker IDs, time stamps, and dialogue.
   
   3. Processes Transcription into Structured Database Fields:
   Incoming webhook data is intercepted by n8n, which processes each new chunk of transcribed dialogue and inserts it into the “dialog” field of the JSON-based entry in the Postgres (Supabase) database.
   
   4. Smart AI Triggering Based on Keywords:
   A powerful conditional check within the workflow watches for key terms in the conversation — for example, if the speaker says “Jimmy,” the system routes dialogue to an OpenAI assistant via the API and prompts it to analyze or summarize the context.
   
   5. Generates AI-Powered Summary Notes:
   When specific trigger conditions are met, the workflow submits the dialogue thread to OpenAI’s Assistant API for analysis. It generates useful notes or summaries which are then injected into the “notes” field of the database record.
   
   6. Maintains Full Thread Context:
   This workflow uses OpenAI’s Chat / Assistant Thread API, meaning every interaction maintains historical context across the whole meeting. This allows the AI to reason over previous dialogue when offering insights or creating summaries.
   
   7. Real-Time Updates:
   Every decision, transcription update, or AI-generated note is recorded with timestamps, speaker names, and relevant metadata. The live updates mean you never have to wait until the end of the meeting to get value — you’re always a step ahead.
   
   Tech Stack and APIs Used
   
   This workflow seamlessly integrates several modern platforms, tools, and APIs:
   
   🟢 n8n – Low-code automation platform that orchestrates the workflow
   🔷 Recall.ai – Used to join meetings and collect audio streams
   🟡 AssemblyAI – Handles the speech-to-text transcription
   🧠 OpenAI Assistant API – Used for generating insights and responses
   🟤 Supabase (PostgreSQL) – Acts as the real-time database storing structured inputs and outputs
   
   User Experience: How Does It Work?
   
   This setup is designed with ease and flexibility in mind. Here's what happens when a meeting is initiated:
   
   Step 1: A meeting URL is captured (manually or programmatically).
   Step 2: The bot is scheduled to join the meeting through Recall.ai.
   Step 3: As the call progresses, each transcribed phrase is sent to n8n via webhook.
   Step 4: Transcription segments are appended to a database entry using a JSON field called dialog.
   Step 5: If a specific term (e.g., “Jimmy”) appears, a trigger directs the transcription to OpenAI.
   Step 6: OpenAI interprets context and generates a note or summary text.
   Step 7: That AI-generated note is once again stored in your Supabase database under a separate “notes” field.
   
   Benefits of This Automation
   
   - 📝 No more manual note-taking — focus on the conversation
   - 💡 Actionable AI insights delivered in real-time
   - 🧠 Persistent context through OpenAI's thread-based API
   - 📁 Organized database structure using JSON columns
   - 🧑‍💼 Ideal for sales teams, coaches, product managers, and consultants
   
   Getting Started
   
   To replicate or customize this workflow, all you need is:
   - A Zoom or Google Meet link
   - API credentials for OpenAI, Recall.ai, and Supabase
   - n8n instance (hosted or self-managed)
   
   The original workflow even features sticky notes and layout comments embedded in n8n to guide you through replacing credentials, setting your meeting links, and performing local development.
   
   Conclusion
   
   This AI-powered meeting bot is a showcase of how far no-code and low-code solutions can go when combined with the right APIs. By automatically transcribing conversations, identifying meaningful points, and responding intelligently, you save hours and empower your team with clarity, accountability, and laser-focused takeaways.
   
   Whether you're a solopreneur, a remote team lead, or a tech-savvy executive, this solution demonstrates the power and flexibility of real-time AI meeting automation — made possible with n8n and a few powerful APIs.
   
   —
   
   Third-Party APIs and Services Used:
   - Recall.ai — for joining and recording/transcribing virtual meetings
   - AssemblyAI — for speech recognition and real-time transcription
   - OpenAI Assistant API — for analyzing conversations and creating AI-powered insights
   - Supabase (via Postgres API) — to store and retrieve structured meeting data and notes
   
   —
   
   Ready to build this yourself or see it in action? Watch the full video tutorial here:  
   📺 https://www.youtube.com/watch?v=rtaX6BMiTeo
   
   For more automations like this, visit the 5minAI community:  
   🔗 https://www.skool.com/5minai
   
   Created by Mark Shcherbakov – Connect on [LinkedIn](https://www.linkedin.com/in/marklowcoding/).

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.