AI Business Insight Generator

Turn KPI signals and market context into structured strategic insights using AI.

AI-powered analytics tool combining LLM reasoning + structured KPI signals + evidence data to generate explainable business diagnostics.

Live Demo

Try the AI Business Insight Generator here: 👉 Launch Live Demo Built with Superun (vibe-coding workflow prototype).

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Key Features

• AI-powered business diagnostics
• KPI trend visualization from uploaded datasets
• clarification question workflow to reduce ambiguity
• evidence-based reasoning using structured KPI signals
• explainable confidence scoring
• exportable consulting-style reports (Markdown / DOCX)

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Quick Start

Run the application locally using Streamlit.

Step 1 - Clone the repository

git clone https://github.com/jiamanlee/2026_AI_Business_Insight_Generator.git
cd 2026_AI_Business_Insight_Generator

Step 2 - Install required dependencies

pip install -r requirements.txt

Step 3 - Set your OpenAI API key

Before running the application, set your OpenAI API key:

export OPENAI_API_KEY=your_openai_api_key_here

You can obtain an API key from:

https://platform.openai.com

Step 4 - Run the Streamlit app

streamlit run app.py

The application will open automatically in your browser:

http://localhost:8501

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The Problem

Many teams monitor KPIs but struggle to translate signals into clear strategic insights.

Typical workflow today:

• Export KPI dashboards
• Manually analyze trends
• Compare with market context
• Write strategy notes or reports

This process is slow, inconsistent, and difficult to scale.

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The Solution

AI Business Insight Generator combines:

• Structured KPI signals
• Market context
• Evidence data
• LLM reasoning

to automatically generate a structured business insight report.

The system also provides:

• KPI trend visualization
• evidence-based KPI summaries
• explainable confidence scoring

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How It Works

Input

Users provide:

• KPI signals
• industry environment change
• market context
• uploaded KPI datasets

Output

The system generates:

• structured business insight report
• KPI trend charts
• evidence summaries
• analysis confidence score

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Product Walkthrough

1. Business Context Input

Users provide key business signals including:

• business type
• target market
• core business problem
• industry environment change
• KPI changes
• additional context

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2. Clarification Questions

The system first generates clarification questions to reduce ambiguity before producing the final report.

This improves reasoning quality and helps the AI produce more accurate insights.

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3. Evidence Data Upload

Users can upload KPI datasets (CSV / Excel), such as:

• new_signups
• conversion_rate
• active_users
• revenue
• upgrade_rate

The system automatically:

• detects date columns
• detects KPI metrics
• summarizes KPI trends

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4. KPI Trend Visualization

Uploaded KPI data is automatically visualized through:

• time-series trend charts
• trend summary tables
• KPI change metrics

This helps validate insights using actual performance signals.

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5. AI Insight Report

The system generates a structured report including:

• key problem diagnosis
• potential root causes
• strategic recommendations
• risk signals

The output is designed to resemble a consulting-style strategy memo.

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6. Explainable Confidence Score + Export

Each report includes a confidence score (0–7) based on:

• business context completeness
• KPI specificity
• numeric KPI signals
• clarification answers
• evidence data
• data usability

Reports can be exported as:

• Markdown
• DOCX

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Tech Stack

• Python
• Streamlit – interactive analytics interface
• OpenAI API – LLM reasoning engine
• Pandas – evidence data parsing and KPI analysis
• Markdown / python-docx – report generation and export

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Architecture

The system follows a structured reasoning pipeline combining user input, evidence data, and LLM-based analysis.

User Input
      ↓
Clarification Question Generation (LLM)
      ↓
User Answers
      ↓
Evidence Data Parsing (Pandas)
      ↓
KPI Trend Visualization
      ↓
Business Insight Generation (LLM)
      ↓
Confidence Scoring
      ↓
Report Rendering & Export

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Example Use Cases

This tool can be used for:

• Product teams diagnosing KPI changes
• Growth teams analyzing conversion trends
• Strategy teams evaluating market shifts
• Startup founders understanding early product signals
• Operations teams summarizing business performance

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Related Projects

More analytics projects:

• Hotel Revenue Intelligence Dashboard
• WTD Analytics & Trend Tracker
• Top-of-Funnel Spend Optimization (MMM)

GitHub Portfolio:

https://github.com/jiamanlee