How to build an Earnings Call Insight and Sentiment Analyzer
StackAI’s analyzer ingests earnings-call audio or transcripts, extracts key themes and cited KPIs, tags sentiment by speaker, and produces a one-page brief teams can share right after the call.
Challenge
Earnings-call transcripts are long and complex. Manual review takes hours, misses tonal cues, and produces inconsistent notes, slowing down investment decisions.
Industry
Finance
Department
Finance
Integrations
OpenAI
Whisper
TL;DR
- What it does: Summarizes earnings calls, highlights themes and KPIs, and tags sentiment per speaker. 
- Who it’s for: Investment analysts, PMs, and IR teams needing fast post-call insights. 
- Time to value: ~30–45 minutes to configure; minutes per new call. 
- Output: A structured PDF/markdown brief with themes, KPI table, sentiment dashboard, and key quotes. 
Common Pain Points of Analyzing Earnings Calls
- Manual parsing takes hours and misses tonal cues. 
- Inconsistent notes and formats across analysts. 
- No quick way to compare sentiment across speakers/quarters. 
- Quotes lack source context for compliance-ready sharing. 
What the Agent Delivers
- Theme & KPI extraction with cited references. 
- Sentiment by speaker (positive, neutral, negative) with per-section scores. 
- Key quotes with timestamps and speaker attribution. 
- A shareable one-pager plus CSV/JSON exports for dashboards. 
Step-by-Step Build (StackAI nodes)
1) Input: Upload Earnings Call Audio (Audio)
What it does: Lets you upload the call recording, either from a file (e.g., MP3/WAV/MP4) or a URL. You can also choose the transcription provider (e.g., Deepgram, Whisper) and model/submodel best suited for accuracy.
Goal: Capture the raw audio source so it can be transcribed and prepared for analysis.

2) Extract Themes, Sentiment and KPIs (LLM)
What it does: This node is an AI agent designed to analyze the transcript of an earnings call and produce a structured, one-page summary with key insights.
Model: GPT-5 (OpenAI) - Best at handling long transcripts.
Instructions
Prompt

3) Structured Summary and Sentiment Dashboard (Output)
Purpose: Displays the structured summary and sentiment dashboard generated by the LLM.




