Community-powered marketplace for custom design goods and home décor
Minted operates a curated artist marketplace for made-to-order stationery, art, and home goods, powered by independent creator submissions and a data-driven curation engine. The tech stack reveals a modernization in progress: React + GraphQL + microservices-oriented architecture on the frontend, Python + FastAPI on the backend, with aggressive adoption of LLM platforms (OpenAI, Anthropic, Azure OpenAI, Google AI) and AI coding tools (Cursor). Current hiring velocity is accelerating with engineering-led growth (4 of 6 active roles), and the project backlog is heavily weighted toward AI-driven automation across HR, operations, and internal tooling — signaling a shift from manual operational overhead toward AI-assisted workflows.
Minted is a San Francisco-based e-commerce marketplace connecting independent artists with consumers seeking premium, customizable design products for life events (weddings, births, holidays). The company operates a hybrid model: a crowdsourced artist community submits designs; data and curation determine which reach the storefront; on-demand manufacturing fulfills orders. The platform spans omnichannel retail (direct-to-consumer e-commerce plus integrations), with 201–500 employees and a focus on mass customization technology. Founded in 2007 and privately held, Minted is backed by established venture firms and operates from downtown San Francisco.
Frontend: React, JavaScript, GraphQL, Webpack, Babel. Backend: Python, TypeScript, Flask, FastAPI. Infrastructure and tooling: Salesforce, Zendesk, Jira, Okta, n8n. LLM platforms: OpenAI, Anthropic, Google AI, Azure OpenAI. Recently adopting Cursor for AI-assisted development.
AI-driven automations for HR and operations, intelligent service desk assistants, custom AI agents for task automation, product catalog experience updates, testing automation, API and micro-frontend architecture, and platform modernization (migrating from monolithic to microservices).
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Minted's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.