n8n + Google Gemini AI — Processing Zoom Transcripts

Processing Zoom transcripts with n8n and Google Gemini AI enables you to build automated backend workflows that analyze meeting recordings, generate summaries, and create action plans without writing code. How the workflow operates: n8n's canvas-based interface allows you to connect Zoom's API to retrieve meeting transcripts, then route them to Google Gemini AI for natural language processing. The AI analyzes conversation content, extracts key insights, and generates structured summaries with actionable items. According to industry analysis by Gartner, automation platforms like n8n have seen adoption increase by over 200% among teams seeking to streamline meeting workflows. Real-world application: Practitioners report setting up workflows where Zoom recordings trigger automatically once meetings end. The transcript flows through n8n to Google Gemini AI, which identifies discussion topics, decisions made, and follow-up tasks. The processed output can be sent to project management tools, email, or databases—all without manual intervention. This approach particularly benefits remote teams conducting multiple meetings daily, transforming hours of recorded conversations into digestible summaries and clear action items within minutes of meeting completion.

n8n is a no-code automation platform with a visual canvas interface that lets you build backend workflows without programming knowledge, including API integrations, authentication handling, and database connections. Why it works for transcript processing: The platform provides pre-built nodes for connecting to services like Zoom and Google Gemini AI. You drag and drop these nodes onto a canvas, configure credentials, and define how data flows between services. This visual approach makes complex backend automation accessible to non-developers while remaining powerful enough for technical users. Key capabilities for this use case: n8n handles API authentication with OAuth flows, manages rate limits, processes JSON data structures, and transforms information between different formats. The platform supports conditional logic, so you can route transcripts based on meeting duration, participant count, or specific keywords detected in conversations. Experienced users report completing functional workflows in 30-60 minutes that would traditionally require days of custom coding. The platform's self-hosted option also means sensitive meeting transcripts can remain within your own infrastructure rather than passing through third-party services.

Step 1 - Zoom Trigger: Configure a Zoom trigger node in n8n that activates when recordings become available. You'll authenticate with Zoom's OAuth and set parameters for which meetings to monitor—specific users, recurring meetings, or all organizational recordings. Step 2 - Transcript Retrieval: Add a Zoom node to fetch the actual transcript file. Zoom provides transcripts in VTT or SRT format, which you'll need to extract and convert to plain text. Use n8n's built-in code node or text processing functions to clean formatting markers and prepare the content. Step 3 - Google Gemini AI Processing: Connect a Google Gemini AI node with your API credentials. Structure your prompt to include the transcript text and specific instructions—for example, "Analyze this meeting transcript and provide: 1) Three-sentence summary, 2) Key decisions made, 3) Action items with assigned owners." Step 4 - Output Formatting: The AI response flows to formatting nodes that structure results for your destination. This might involve splitting action items into individual task entries, extracting dates and assignees, or converting the summary into HTML for email distribution. Step 5 - Delivery: Route the processed output to your chosen platforms—Slack channels, email recipients, project management tools like Asana or Notion, or databases for archival. Each destination requires its own node with appropriate authentication and field mapping. Practitioners recommend testing with a single meeting first, refining prompts based on output quality, then enabling automatic processing once results meet expectations.

Google Gemini AI can generate comprehensive meeting summaries and structured action plans by analyzing conversation patterns, identifying decisions, and extracting commitments from transcript text. Summary capabilities: The AI produces executive summaries highlighting main discussion topics, key arguments presented, and consensus reached. It identifies speakers' contributions (when names are in the transcript), tracks topic transitions, and notes important questions raised. Summaries typically range from three sentences for brief check-ins to multi-paragraph overviews for strategic sessions. Action plan extraction: Gemini AI identifies specific commitments by recognizing linguistic patterns like "I'll handle," "we need to," or "by next week." It extracts who committed to what actions, identifies deadlines mentioned, and flags dependencies between tasks. The AI can categorize action items by urgency, department, or project phase based on context. Additional analysis options: Users structure prompts to extract sentiment analysis (team morale, concern areas), decision rationale (why specific choices were made), risks mentioned, and open questions requiring follow-up. The AI can identify recurring themes across multiple meeting transcripts, useful for tracking project evolution. One limitation to consider: accuracy depends on transcript quality. Background noise, multiple simultaneous speakers, or poor audio in Zoom recordings can result in garbled transcripts that confuse the AI analysis. Clean audio input significantly improves output quality.

Zoom authentication setup: Navigate to Zoom Marketplace and create a Server-to-Server OAuth app or OAuth app depending on your use case. Server-to-Server works for organizational automation without user interaction. You'll receive an Account ID, Client ID, and Client Secret. In n8n's credentials panel, add new Zoom OAuth2 credentials and paste these values. n8n handles token generation and refresh automatically. Google Gemini AI authentication: Access Google AI Studio or Google Cloud Console to generate an API key for Gemini AI. For production workflows, create a service account with appropriate permissions rather than using personal API keys. In n8n, add Google Gemini AI credentials using the API key authentication method. Store the key securely in n8n's encrypted credentials storage. Security best practices: Never hardcode credentials directly in workflow nodes. Use n8n's credential system which encrypts sensitive data and allows credential reuse across multiple workflows. Set up separate credentials for development and production environments. Regularly rotate API keys and monitor usage through Zoom and Google Cloud dashboards. Permission configuration: Ensure your Zoom app has recording access scopes: recording:read, meeting:read, and potentially cloud_recording:read depending on your setup. For Google Gemini AI, verify API quotas are sufficient for your expected transcript volume—processing long transcripts consumes more tokens than shorter ones. Organizations handling sensitive meeting content should review data processing agreements for both services and consider running n8n self-hosted rather than using cloud instances to maintain data sovereignty.

Yes, you fully control the analysis focus by customizing the prompts sent to Google Gemini AI through n8n, allowing you to extract business-specific insights from Zoom transcripts. Prompt engineering for custom analysis: Structure your Gemini AI prompt to request specific information formats. For sales meetings, ask for: lead qualification signals, objections raised, competitive mentions, and next steps. For product development, request: feature requests, bug reports, user pain points, and technical constraints mentioned. For executive meetings, extract: strategic decisions, resource allocations, risk assessments, and timeline commitments. Template-based extraction: Create structured output templates in your prompt. For example: "Extract information in this exact format: DECISIONS: [list], BLOCKERS: [list], BUDGET_MENTIONS: [list with amounts], DEADLINES: [list with dates]." This ensures consistent output structure that's easily parsed by downstream systems. Industry-specific customization: Users in healthcare configure prompts to identify compliance discussions and patient care decisions. Legal teams extract contractual commitments and liability concerns. Marketing teams track campaign performance mentions and content ideas. The AI adapts its analysis based on the terminology and patterns you specify. Iterative refinement: Start with broad analysis requests, review actual outputs, then refine prompts to reduce irrelevant information and emphasize critical details. Experienced practitioners maintain prompt libraries for different meeting types—standups, client calls, retrospectives—each optimized for that context. You can also chain multiple AI calls in n8n: one for summary, another for sentiment analysis, and a third for specific data extraction, combining outputs into comprehensive meeting intelligence.

Transcript availability delays: Zoom doesn't generate transcripts instantly after meetings end. Processing typically takes 5-30 minutes depending on recording length. Build polling logic into your n8n workflow or use webhook notifications rather than expecting immediate availability. Add wait nodes or schedule checks at intervals. Token limits and long transcripts: Google Gemini AI has input token limits. A 90-minute meeting transcript might exceed context windows. Solution: implement chunking logic in n8n that splits long transcripts into segments, processes each separately, then combines summaries. Alternatively, use Gemini's larger context models designed for document processing. Inconsistent transcript quality: Background noise, accents, and technical jargon cause transcription errors. These cascade into AI analysis mistakes. Mitigation: add a preprocessing step that identifies low-confidence words (often marked in Zoom transcripts), implements spell-checking for common terms in your domain, or includes context in your AI prompt like "this is a technical meeting about software development—interpret ambiguous terms accordingly." Cost management: Processing every meeting transcript through AI APIs accumulates costs. Implement filters in n8n: only process meetings longer than 15 minutes, skip recurring standup calls, or allow meeting organizers to tag recordings for processing. Monitor API usage through Google Cloud Console dashboards. Rate limiting: Both Zoom and Google APIs implement rate limits. Build retry logic with exponential backoff in n8n. The platform supports error handling workflows that catch rate limit responses and queue items for later processing. Users report the most common early mistake is overcomplicating workflows. Start minimal—Zoom → Gemini → Email—then add sophistication incrementally as you understand actual needs versus assumed requirements.