KnotMyShop

Inspiration

Most storefronts are one-size-fits-all. Merchants can segment users, but they still can’t truly personalize the experience in real time for each shopper. We wanted to make personalization practical by using digital twins built from past purchase behavior, then adapting the storefront and offers instantly.

What it does

Knot My Shop creates a digital twin for each shopper based on historical purchase signals and behavior patterns, then:

-Applies subtle, dynamic UI/UX changes per shopper (layout emphasis, product ordering, messaging, urgency cues).

-Personalizes recommendations and flow paths.

Generates custom discounts tailored to predicted intent and conversion likelihood. Helps merchants improve conversion without redesigning the whole store.

How we built it

We built KnotMyShop as a Shopify-native app with a real-time personalization pipeline:

Frontend dashboard: Next.js, React, TypeScript, Tailwind for merchant controls and live insights. Runtime personalization: Canvas + SSE-driven real-time updates for simulation and preview. Backend engine: FastAPI + asyncio services to generate twin opinions, score outcomes, and serve personalization decisions. AI layer: K2-Think-v2 (OpenAI-compatible) with custom prompting for twin generation and offer fit. Data layer: Supabase/Postgres for profiles, twin states, outcomes, and discount decision logs. Shopify integration: Theme App Extension + App Proxy for storefront-level insertion. External signals: Knot API + Knot JS SDK for account linking and transaction-informed enrichment.

Challenges we ran into

Integrating personalization logic cleanly into Shopify’s extension/proxy model.
Turning mixed agent/twin outputs into stable, merchant-safe UI decisions.
Keeping discount personalization aggressive enough to convert, but controlled enough to protect margins.
Ensuring real-time behavior stayed fast and consistent across storefront sessions.

Accomplishments that we're proud of

Built a fully working Shopify app with production-ready architecture.
Shipped digital twin-driven personalization that can alter UI/UX at shopper level. -Implemented custom discount logic based on predicted behavior and purchase history. -Got swarm/twin systems communicating reliably with the merchant-facing UI.

What we learned

Transaction history dramatically improves personalization quality when paired with behavioral modeling.
In commerce, trust requires explainable personalization and guardrails.
Shopify app architecture demands careful planning for speed, reliability, and maintainability.