MerchPulse: Stopping Refund Bleed for Shopify Sellers with Open-Source AI

Saima Aliya — Sat, 17 Oct 2026 21:43:24 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

MerchPulse is an autonomous customer feedback intelligence and root-cause platform built specifically for e-commerce merchants (Shopify, Amazon, WooCommerce, and Etsy sellers).

The Problem

Small and mid-sized e-commerce sellers are flooded with scattered, messy feedback across:

Product reviews (Shopify, Amazon)
Helpdesk tickets (Zendesk, Gorgias)
Live chat transcripts
Return reasons and refund notes Merchants spend 10–15 hours every week manually reading hundreds of comments trying to guess whether their biggest issue is carrier delays, sizing inaccuracies, checkout bugs, or supplier defects. By the time they spot a pattern manually, thousands of dollars in refunds and ad spend are already lost.

Demo

Live Deployed App: https://merchpulse.onrender.com
Demo Dataset Included: includes dashboard with KPI cards, the charts and the root-cause and action plan tab.

Code

GitHub Repository: https://github.com/saimaaliya2005-cypher/merchpulse.
Stack: HTML5, Tailwind CSS, Chart.js, PapaParse, Gemma 2, Prior Labs TabPFN 2.5, Render.

How I Built It

MerchPulse combines a dual-model open-source AI architecture:

Google Gemma 2 (Open-Weight LLM): Acts as the semantic reasoning engine. It ingests unstructured text, classifies sentiment (Positive, Neutral, Negative, Frustrated), groups complaints into 6 operational categories, and extracts concrete root-cause explanations backed by direct customer quotes.
Prior Labs TabPFN 2.5 (Tabular Foundation Model): Acts as the tabular risk engine. When a merchant uploads a CSV with order metadata (rating, order_value, shipping_days, product_sku), TabPFN analyzes tabular patterns to detect anomalies (e.g., transit threshold correlation where orders taking >6 days experience a 5x return spike) and predicts high-risk return SKUs without needing days of model training.

Why Does Open Innovation Matter?

Customer PII & Strict Data Sovereignty: Customer feedback contains sensitive personal identifiable information (names, delivery addresses, order numbers, payment amounts). Sending this proprietary data to closed third-party APIs (like OpenAI or Anthropic) introduces compliance risks and data privacy concerns. Because Gemma 2 is open-weight, merchants can run inference locally or in a private cloud container where zero customer data leaves their infrastructure.
Sustainable Margins vs. Closed API Lock-in: Closed APIs charge per token. For an e-commerce brand processing 5,000+ support tickets and reviews monthly, token fees quickly eat into thin retail margins. Open-weight models cost essentially zero per request once deployed on standard commodity compute.
Domain Adaptation & Fine-Tuning: Closed models cannot be adapted at the weight level without expensive enterprise agreements. Open innovation allows merchants to fine-tune Gemma on niche e-commerce vocabularies (apparel sizing tolerances, tech specs, shipping codes) that generic APIs frequently misinterpret.

My First User

I don’t have paying customers yet, but I've tested the platform on a realistic 15-order test dataset modeled after a Shopify apparel store.

The test proved the concept immediately: Gemma and TabPFN automatically connected 4 separate complaints—totaling $514 in return requests—to a single 4mm zipper defect on Wool Blend Jackets (SKU JKT-WOOL-M). Instead of an admin spending hours manually reading tickets, the root cause and supplier fix were ready in seconds.

Prize Categories

Best Use of Gemma ($200) — Employs Google's open-weight Gemma 2 for root-cause synthesis, sentiment extraction, and executive recommendation generation.
Best Use of TabPFN ($200) — Leverages Prior Labs' TabPFN tabular foundation model for anomaly detection, transit time correlation, and return probability predictions.
Best Use of Render ($200) — Deployed via render.yaml infrastructure blueprint on Render.

DEV Community: Saima Aliya