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Retail Technology & eCommerceRef #pric

Price Intelligence AI – Retail Pricing Platform

AI-powered competitive pricing intelligence platform for US retail and eCommerce — automating competitor research, delivering real-time market visibility, and enabling faster data-driven pricing decisions.

# React.js# Node.js# PostgreSQL# OpenRouter AI# Railway
Price Intelligence AI competitive pricing platform for US retail and eCommerce

Manual Research Time

Reduced

Manual Research Time

Reduced

Pricing Decision Speed

Faster

SaaS Architecture

Scalable

The Challenge

Fragile Legacy Systems

Manual competitor research, constant price fluctuations, aggressive competition, and delayed pricing decisions reducing profit margins for US retail and eCommerce businesses.

  • 1

    Manual competitor price monitoring consuming hours of daily research.

  • 2

    Delayed pricing decisions causing lost sales to faster competitors.

  • 3

    No centralized view of market pricing trends and competitor strategies.

The Solution

Quantum Engineering

We built a scalable SaaS pricing intelligence platform with automated competitor crawling, AI-driven pricing recommendations via OpenRouter, and real-time market dashboards.

  • Automated competitor price monitoring across thousands of SKUs.

  • AI-powered pricing recommendations optimizing margins in real time.

  • Centralized market intelligence dashboard for data-driven decisions.

What We Built

Core Features

Competitor Monitoring

Automated crawling of competitor prices across multiple marketplaces in real time.

AI Pricing Engine

OpenRouter AI analyzes market data and recommends optimal pricing strategies.

Market Intelligence

Comprehensive dashboards showing pricing trends, competitor movements, and market positioning.

Real-time Alerts

Instant notifications when competitors change prices on key products.

Margin Analytics

Track how pricing decisions impact margins and revenue in real time.

SaaS Architecture

Scalable multi-tenant SaaS built on Railway with PostgreSQL for high-volume data.

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