/* TEAM */
Creator: Sayuri M. — https://sayurimizuguchi.com
Company: MARTINS HIGH QUALITY SOFTWARE LTDA
Trade Name: AnkiCat
Site: https://ankicat.com
Mission: Make studying more effective and less tedious through AI-powered flashcards and evidence-based learning techniques. Less busywork. More remembering.
Location: São Paulo, SP, Brazil

Engineering: Sayuri M. (CEO & Principal Software Engineer), Rolph F. (Senior Mobile Developer), Fabiano L. (Engineering Team), Arthur Jr. (Engineering Team)
Design: Kaori M. (UX/UI Designer & Digital Artist)
QA: Sabrina A. (QA Tester)

/* THANKS */
Contributors: Everyone who joined the waitlist and gave early feedback, the open-source community, and the researchers whose work on spaced repetition, active recall, and cognitive psychology made this possible.

/* SITE */
Language: English, Portuguese
Standards: HTML5, CSS3, WAI-ARIA, WCAG 2.1 AA
Components: React 19, Vite 6, TypeScript, Tailwind CSS 4, Motion
Hosting: Cloudflare Workers
Last update: 2026-06-27

/* TECHNOLOGIES */
Frontend: React 19, Vite 6, TypeScript (strict mode), Tailwind CSS 4, Motion (animation library), React Router 7
Backend: Cloud Run (Go), Cloudflare Workers (TypeScript)
Storage: Cloudflare R2
Mobile: React Native (iOS & Android)
Security: Cloudflare Turnstile, Apple Sign In
CI/CD: Cloudflare Workers Builds
Design tokens: Figma → TypeScript generation pipeline

/* OPEN SOURCE */
AnkiCat is grateful to the following open-source projects and libraries:
- React (Meta)
- Vite (Evan You & Vite contributors)
- TypeScript (Microsoft)
- Tailwind CSS
- Motion (formerly Framer Motion)
- Cloudflare Workers
- React Router
- Vitest
- Playwright
- ESLint
- Bun
- Wrangler
- Fontsource
- vite-imagetools
- fontaine
- canvas-confetti
- html2canvas

/* SUPPORT */
Human support: Real Analyst CS Support team. No chatbots, no automated deflection. Every question answered by a person who builds or tests the product.

/* AI CONSCIENCE */
Only 1 AnkiCat feature uses generative AI (deck drafting). Subscribers get limited credits; extras purchased per use (Stripe) with a monthly cap. Training GPT-3 evaporates 700,000L of freshwater. Global AI projected to withdraw 4.2-6.6 billion cubic meters of water in 2027 (more than 4-6 Denmarks combined). Source: Li, Yang, Islam & Ren (2023). Making AI Less Thirsty. arXiv:2304.03271 — github.com/Ren-Research/Making-AI-Less-Thirsty

/* CONTACT */
Email: support@ankicat.com
GitHub: https://github.com/sayurimizuguchi