QuartQ

Inspiration

Everyday purchases from quick commerce and e-commerce platforms generate valuable personal spending data. but most people never truly track or analyse it. We saw an opportunity to transform scattered invoices into actionable intelligence for households and consumers.

What it does

QuartQ is an AI-powered retail intelligence platform that reads purchase invoices across formats the PDFs, images, and text, and converts them into structured knowledge graphs. It helps users understand spending habits, retrieve historical purchases instantly, and generate meaningful insights from everyday shopping behaviour.

How we built it

QuartQ was built using Knowledge Graph architecture, Agentic AI workflows, and N8N automation pipelines. The system processes multi-format invoices, extracts contextual purchase data, and stores relationships intelligently for real-time querying and insights.

Challenges we ran into

One of the biggest technical challenges was designing a unified pipeline capable of handling diverse invoice formats consistently, including scanned images, PDFs, and raw text invoices — while maintaining extraction accuracy and contextual understanding.

Accomplishments that we're proud of

We successfully built a system that not only tracks purchases but also delivers intelligent summaries, spending insights, and decision-making support. QuartQ turns raw shopping history into a searchable, insight-driven retail memory layer for users.

What we learned

Building QuartQ taught us how to create scalable Agentic AI workflows, manage contextual memory using knowledge graphs, and design automation systems capable of handling unstructured real-world retail data.

What's next for QuartQ

Our next step is to validate the product with real users, gather feedback, and evolve QuartQ into a consumer-first retail intelligence assistant. We envision QuartQ becoming a daily utility for families, household shoppers, and frequent online buyers who want smarter visibility into their spending habits.