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