VVyshyvka
studio

Case study

Slovka

A multilingual vocabulary platform linking public lessons and protected study workflows to progress tracking, cultural-text ingestion, image generation, and AI-assisted vocabulary pipelines.

Capabilities
Design, Frontend, Backend, DBA, AI/ML, Content Ops
Type
Language-learning product system
Stack
Next.js, React, TypeScript, Drizzle ORM, Cloudflare
Scope
Learning pages, Learner tools, Content ops, AI pipelines
Year
2025

Product inventory

Recorded scope across routes, data models, interface screens, and workflow code.

Route pages
19
Drizzle tables
38
Source files
2,396
TypeScript files
1,781
Locales
4

Project overview

A multilingual vocabulary platform linking public lessons and protected study workflows to progress tracking, cultural-text ingestion, image generation, and AI-assisted vocabulary pipelines.

Ukrainian vocabulary study spans collections, nested lists, words, translations, grammar references, images, quizzes, progress, goals, and reading lessons. The same records also support generation jobs and review workflows, while learner-facing pages remain focused on study.

What was built

Songs and poems yield candidate vocabulary records that can be reviewed before appearing in lists, quizzes, grammar references, and reading lessons.

Public learning pages

Landing, about, grammar case, and preview routes introduce structured language learning content.

Learner workspace

Protected collections, lists, words, quizzes, progress, goals, calendars, and kids reading pages.

Content operations

Admin and tool routes manage collection generation, song import, lyric and poem processing, image regeneration, review, reconciliation, and deduplication.

Pipeline infrastructure

Graph-based pipelines crawl carols and poems, extract dictionary-form vocabulary, translate, reconcile existing words, queue imagery, and persist collections; one carol run processed 19 songs.

Pipelines

Artifacts, gates, telemetry, reconciliation.

Media storage

R2-backed image workflows.

Selected details

Implementation details from the product, data, workflow, and infrastructure layers.

  • 01

    Vocabulary is modelled through collections, lists, words, translations, documents, and membership tables.

  • 02

    Learning state includes quizzes, statuses, study plans, goals, and calendar data.

  • 03

    Song pipelines crawl carol sources, preserve lyrics and metadata, and turn each song into a bilingual vocabulary list.

  • 04

    Lyric and poem pipelines chunk longer texts, extract learner-facing vocabulary, standardize entries into dictionary forms, deduplicate candidates, and reconcile against existing words.

  • 05

    One Christmas carol run processed 19 songs and 1,612 source words; a poem-length run consolidated 328 raw vocabulary candidates into 288 unique entries before persistence.

  • 06

    Image-generation workflows create consistent vocabulary imagery, upload assets, and attach generated documents back to accepted word records.

  • 07

    Artifacts, schemas, review gates, reconciliation, image queues, and persistence steps control how AI-generated content enters the product.

  • 08

    Cloudflare deployment uses Workers, D1, R2, and OpenNext-oriented application structure.

  • 09

    Learner-facing routes and internal tools stay separated while sharing the same vocabulary, image, and collection records.

Systems overview

How the public pages, staff workflows, data, review steps, and infrastructure connect.

Learning pages

Landing, grammar, previews.

Content graph

Collections, lists, words, translations.

Learner tools

Quizzes, progress, goals, kids reading.

Operations

Admin, import, generation, deduplication, review.

Pipeline layer

Crawling, extraction, image queues, persistence.

Technology

Languages, frameworks, data stores, and infrastructure.

Next.js
React
TypeScript
Tailwind CSS
Drizzle ORM
NextAuth
Cloudflare Workers
Cloudflare D1
Cloudflare R2
OpenNext

Design language

A tactile Ukrainian study system: literary typography, floral framing, pale peach fields, sage and coral accents, circular word imagery, and denser review tools for content operations.

Philosophy

Study pages with operational depth

The public learning pages stay warm and tactile while the same content model supports protected progress, review queues, image generation, and pipeline persistence.

Floral study framesCircular word mediaTabbed grammar panelsReview-gated generation
Aa
Serif display, text, Fira CodeStudy pages, vocabulary, pipeline state
Ink#28332d
Paper#fff7ef
Sage#9caf88
Coral#db715c
Blush#f4d6c8

Selected screens

More screens showing the product in use.