Building InsightForge: AI-Powered Business Intelligence with Aurora PostgreSQL

ADITYA JHA — Sun, 21 Jun 2026 07:40:02 +0000

Building InsightForge: An AI-Powered Business Intelligence Platform with Aurora PostgreSQL and Generative AI

H0Hackathon

Built for the Vercel + AWS Database Hackathon.

InsightForge helps small and medium-sized businesses transform raw sales, inventory, and customer data into actionable insights, forecasts, and AI-generated recommendations.

The Problem

Small and medium-sized businesses generate large amounts of data every day through sales, inventory management, and customer interactions. However, most SMBs cannot afford dedicated analysts or expensive business intelligence tools.

As a result, valuable business data often remains unused inside spreadsheets and CSV files.

I wanted to build a platform that could transform raw business data into actionable insights within minutes.

That idea became InsightForge.

What is InsightForge?

InsightForge is an AI-powered Business Intelligence and Decision Support Platform designed for SMBs.

Users can upload business datasets such as:

Sales Data
Inventory Data
Customer Data

The platform automatically analyzes the data and generates:

Business Health Scores
Revenue Insights
Inventory Analytics
Customer Analytics
Risk Detection
Forecasting
AI-Generated Executive Reports

The goal is simple:

Help small businesses make data-driven decisions without needing a dedicated analytics team.

The Challenge

One of the biggest challenges was handling different dataset structures.

Every business stores data differently.

For example:

Revenue may appear as Revenue, Sales, Amount, or Total Value.
Customer identifiers can have different column names.
Inventory datasets vary significantly across industries.

To solve this problem, I built a dataset detection and column mapping system that automatically identifies dataset types and maps important business metrics.

This allows InsightForge to work with a wide variety of business datasets without requiring manual configuration.

Why Aurora PostgreSQL?

Database selection was one of the most important architectural decisions.

InsightForge manages highly related business entities, including:

Users
Uploaded datasets
Business health scores
Forecasts
AI-generated reports

These relationships require:

Relational consistency
Structured querying
Transactional integrity
Historical data tracking

Aurora PostgreSQL was a natural fit because it allows InsightForge to maintain relationships between business records while supporting analytical workloads.

Rather than treating uploaded files as temporary inputs, Aurora enables the platform to build a historical business knowledge base that supports long-term trend analysis and forecasting.

Architecture

Frontend

Next.js
React
Tailwind CSS

Backend

Next.js API Routes
Prisma ORM

Database

Amazon Aurora PostgreSQL

AI Layer

Google Gemini API

Deployment

Vercel

Key Features

Intelligent Dataset Detection

The system automatically identifies whether uploaded data belongs to:

Sales
Inventory
Customer datasets

Business Health Score

A composite score that evaluates:

Revenue performance
Inventory efficiency
Customer readiness
Forecast readiness

Forecasting and Risk Analysis

InsightForge identifies business risks and highlights areas requiring attention.

AI Executive Reports

Business leaders receive natural language summaries and recommendations generated from their data.

Lessons Learned

Building InsightForge taught me several important lessons:

Data quality matters more than model complexity.
Schema design significantly impacts analytics performance.
Relational databases remain essential for business intelligence systems.
AI becomes far more valuable when combined with structured business data.

Future Improvements

Future versions of InsightForge will include:

Multi-user collaboration
Real-time data synchronization
Advanced forecasting models
Industry-specific analytics templates
Automated anomaly detection

Conclusion

InsightForge was built around a simple idea:

Businesses should not need a dedicated analytics team to understand their own data.

By combining Aurora PostgreSQL, Next.js, and Generative AI, InsightForge transforms disconnected business datasets into actionable insights, forecasts, and recommendations that help SMBs make better decisions.

Thanks for reading.
The complete source code is available on GitHub:

aditya2785 / InsightForge

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more…

View on GitHub

You can try the live application here:

insight-forge-kappa.vercel.app

DEV Community: ADITYA JHA