Call Analyzer Insights
Transcribes sales calls with AssemblyAI and uses OpenAI to extract client intent, interest scores, and upsell opportunities into Supabase.
What it does
Get clear insights from your sales calls by automatically transcribing audio with AssemblyAI and extracting client intent, interest scores, and upsell opportunities using OpenAI. The system then saves both the raw transcripts and these actionable metrics in Supabase for your immediate review.
Step by step
- Configure the AssemblyAI node with API credentials and set the webhook URL to receive the transcription callback.
- Define a strict JSON schema in the OpenAI node to enforce structured output for client intent and interest scores.
- Set up the Supabase node to insert records into a dedicated table containing columns for raw text, insights, and metadata.
Where the LLM does the work
- Craft system prompts that define specific criteria for identifying upsell opportunities based on conversational cues rather than explicit keywords.
- Refine the JSON schema examples to ensure OpenAI consistently outputs valid JSON without syntax errors or missing fields.
- Tune the temperature and top_p parameters in the OpenAI node to balance creativity in insight generation with factual accuracy.
Watch out for
Tools that fit
The agent brief
You are helping me build the following AI agent workflow. ## Goal Call Analyzer Insights: Transcribes sales calls with AssemblyAI and uses OpenAI to extract client intent, interest scores, and upsell opportunities into Supabase. ## Specification - What it does: Get clear insights from your sales calls by automatically transcribing audio with AssemblyAI and extracting client intent, interest scores, and upsell opportunities using OpenAI. The system then saves both the raw transcripts and these actionable metrics in Supabase for your immediate review. - Trigger: Triggered by an event (Manual · on new audio file) - Autonomy: Fully hands-off - Expected setup effort: a few focused days - Tools/services involved: - N8N: Workflow orchestration - AssemblyAI: Audio transcription and speaker diarization - OpenAI: Analyzing call transcripts for intent and upsell opportunities - Supabase: Storing transcription and analysis data ## Known pitfalls, handle each one explicitly in your implementation 1. AssemblyAI webhooks may arrive out of order if multiple calls are processed simultaneously, so add a unique call ID field to correlate results correctly. 2. OpenAI responses can occasionally fail JSON validation due to trailing commas or markdown formatting, so implement a retry loop with regex cleanup logic. 3. Supabase insertions might fail if the AI returns null values for required fields, so add default value mappings in the workflow before the database step. ## Reference implementation https://n8n.io/workflows/2547 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/call-analyzer-with-ai-insights/ via usecasesforagents.com
Frequently asked questions
Can I use a different Automation Platform than n8n?
Yes. n8n is only the example automation platform in this recipe. The same flow works with Home Assistant. Swap the automation platform connection and keep the rest of the setup as written.
Want this running in your business?
Who it's for
Built for sales teams who want the prep work done before they pick up the phone.
Support teams facing the same queue pressure can run this exact pattern on their tickets.