AI Call Analysis System is an automation project for converting recorded calls into transcripts, summaries, and structured reports. The workflow uses Whisper for audio transcription, OpenAI for analysis and summarization, Google Sheets for reporting, and n8n or Make.com for orchestration. It is designed for teams that review calls manually and need a more consistent way to extract decisions, follow-up items, objections, questions, and key conversation details. The project focuses on operational analysis rather than surveillance, scoring claims, or invented business outcomes.
Call review is a time-consuming manual process. A manager, operator, or assistant may need to listen to recorded calls, take notes, summarize what happened, identify next steps, and enter findings into a spreadsheet or CRM. This becomes difficult when calls are long, frequent, or handled by multiple people.
Manual notes are also inconsistent. One reviewer may capture action items while another focuses on objections or customer sentiment. Important details can be missed, and reports may be delayed because listening to recordings takes as long as the calls themselves.
This project automates the first pass of call analysis. Audio files are transcribed with Whisper, analyzed with OpenAI, and written into Google Sheets as structured rows. A human can then review the outputs, correct any issues, and use the report for follow-up or process improvement.
The system accepts call audio files from a defined source such as Google Drive, a webhook, or a manually updated folder. The automation sends the audio to Whisper to produce a transcript. OpenAI then analyzes the transcript using a structured prompt that asks for a concise summary, participant intent, action items, questions, objections, risks, and follow-up recommendations.
The final report is written into Google Sheets. Each row can represent one call, with columns for file name, call date, transcript link, summary, key points, action items, sentiment notes, and processing status. This creates a searchable call review database without requiring a fully custom application.
The workflow is designed to support human review. AI-generated summaries are useful for speed, but they should be treated as draft analysis, especially when calls involve sensitive decisions, legal topics, or customer commitments.
Call Audio File
↓
Google Drive / Webhook
↓
n8n / Make.com
↓
Whisper
↓
OpenAI
↓
Google Sheets
↓
Human Review
The audio source provides files for processing. n8n or Make.com controls the workflow and passes files between services. Whisper converts speech to text. OpenAI converts the transcript into structured analysis. Google Sheets stores the report so stakeholders can review and filter results. ## Screenshot
- Whisper
- OpenAI API
- Google Sheets
- Google Drive or webhook input
- Audio transcription
- AI summarization
- Structured reporting
- n8n or Make.com
- HTTP requests
- A call recording is uploaded to a Google Drive folder or submitted through a webhook.
- The automation detects the new audio file and creates a processing record.
- n8n or Make.com validates the file type and prepares the file for transcription.
- The audio file is sent to Whisper.
- Whisper returns a transcript of the call.
- The transcript is stored as text, linked file content, or a field in the processing record.
- OpenAI receives the transcript with a structured analysis prompt.
- The AI returns a summary, key discussion points, action items, questions, objections, and follow-up recommendations.
- The workflow formats the analysis into a consistent schema.
- Google Sheets is updated with a new row for the call.
- The row includes metadata such as file name, source link, transcript status, analysis status, and generated outputs.
- If a transcription or analysis step fails, the row is marked with an error status for review.
- A human reviewer can edit the Google Sheets row, validate the summary, and use the output for follow-up.
- Automated audio transcription with Whisper
- AI-generated call summaries
- Structured extraction of action items and follow-up points
- Google Sheets reporting for easy review
- Status tracking for each processed call
- Support for Drive folder or webhook-based input
- Human-reviewable output format
- Modular workflow design for future CRM integration
- Error handling for failed files or incomplete transcripts
Audio quality is one of the main challenges. Calls may include background noise, overlapping speakers, poor microphone quality, long silences, or multiple languages. Whisper can produce useful transcripts, but the workflow should allow human correction and should not assume perfect accuracy.
Another challenge is transcript length. Long calls can exceed comfortable prompt sizes for analysis. The workflow may need to chunk transcripts, summarize sections, and then generate a final combined analysis. This adds complexity but improves reliability for longer recordings.
Structured reporting requires prompt discipline. If the AI output is inconsistent, Google Sheets becomes difficult to use. The workflow should request predictable sections or JSON-like output, then map those sections into spreadsheet columns. It should also handle missing values gracefully.
Privacy and access control matter in call analysis. Recordings and transcripts may contain personal or business-sensitive information. API credentials should be stored securely, Drive folders should have appropriate permissions, and access to Google Sheets should be limited to relevant users.
The system should also avoid presenting AI summaries as final decisions. The practical implementation should frame the output as an assistant-generated first pass, with humans responsible for validation and any customer-facing follow-up.
Future improvements could include speaker diarization if the transcription provider and audio quality support it. This would make it easier to separate customer statements from internal team responses. The workflow could also support automatic extraction of CRM fields, such as lead status, requested service, budget range, next meeting date, or unresolved objections.
The reporting layer can be extended with dashboards, filters, and review statuses. For example, calls could be marked as reviewed, needs follow-up, or requires manager attention. Notifications could be sent to Telegram, Slack, or email when a call includes urgent follow-up items.
For larger deployments, the system could add queueing, retry logic, transcript storage policies, and role-based access controls. A future version could also connect directly to a CRM so reviewed outputs become part of the customer record.