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NeuralCommerce

AI-powered retail analytics platform serving 200+ NYC businesses with real-time insights and predictive inventory management

200+ Active Customers
$2.1M Annual Revenue
18mo Since Launch

Overview

NeuralCommerce was born from a simple observation: NYC's independent retailers were drowning in spreadsheets while competing against data-driven e-commerce giants. Founder Jessica Santos spent months talking to boutique owners, restaurant operators, and specialty stores across Manhattan and Brooklyn.

The platform combines point-of-sale data, foot traffic patterns, weather forecasts, and local events to provide actionable insights. Within 6 months of launch, NeuralCommerce was processing over $50M in annual retail transactions.

The Problem

📊

Data Overwhelm

Retailers had access to POS data but lacked tools to extract meaningful insights from it.

📦

Inventory Waste

Poor demand forecasting led to overstocking (capital tied up) or stockouts (lost sales).

⏱️

Manual Processes

Business owners spent hours each week in Excel instead of serving customers.

The Solution

NeuralCommerce integrates directly with existing POS systems and provides:

  • Real-time Dashboard: Track sales, inventory, and customer patterns in a beautiful, intuitive interface
  • Predictive Analytics: ML models forecast demand with 87% accuracy, accounting for seasonality and local events
  • Smart Alerts: Get notified when inventory levels need attention or unusual patterns emerge
  • Automated Reporting: Weekly insights delivered to your inbox with actionable recommendations

Technology Stack

Frontend

React TypeScript Tailwind CSS Chart.js

Backend

Python FastAPI PostgreSQL Redis

Machine Learning

PyTorch scikit-learn Pandas MLflow

Infrastructure

AWS Docker Kubernetes GitHub Actions

Development Timeline

Month 1-2

Discovery & Design

User interviews with 40+ retailers, competitive analysis, and initial prototype

Month 3-5

MVP Development

Built core dashboard, POS integrations, and basic analytics features

Month 6-8

Beta Testing

Launched with 12 pilot customers, collected feedback, iterated rapidly

Month 9-12

ML Integration

Developed and deployed predictive models, achieving 87% forecast accuracy

Month 13-18

Scale & Growth

Expanded to 200+ customers, raised seed round, grew team to 12 people

Impact & Results

32%

Average reduction in inventory carrying costs for customers

47%

Decrease in stockout incidents across all clients

8hrs

Saved per week on manual data analysis and reporting

94%

Customer satisfaction score (NPS: 72)

Lessons Learned

🎯 Start with a painful, specific problem

We didn't build "analytics software." We built "a tool that helps NYC boutiques avoid stockouts during Fashion Week." The specificity helped us build exactly what customers needed.

🔌 Integrations are table stakes

Initially, we required manual CSV uploads. Customer adoption skyrocketed once we built direct POS integrations. Make it effortless to get value.

📊 AI is a feature, not the product

Customers don't buy "machine learning." They buy "knowing exactly how much inventory to order next week." Lead with outcomes, not technology.

🤝 Community accelerates everything

XDF connections helped us find our first 10 customers, a technical co-founder, and introductions to investors. Building in a community compounds your efforts.

Team

JS

Jessica Santos

Founder & CEO

XDF Member
LC

Luis Chen

CTO

XDF Member
NK

Nina Kapoor

Head of Product

XDF Member