The problem
Kwéyòl Donmnik is spoken across Dominica and its diaspora, but the only dictionaries were on paper. The question wasn’t how to fix a flawed tool. It was why nobody had built one, and whether it could be done quickly.
How we built it
Data first
With no digital dataset, an OCR pipeline pulled entries from the printed dictionary.
Two models, checking each other
Claude and Gemini each structured the entries and cross-checked the other, catching OCR errors either one alone might miss. Disagreements were flagged for manual review.
Interface last
The screens were designed only after the data was verified, so the design is built around real entries, not placeholders.
Scope held to ship
A three-hour limit, set at the start, kept v1 focused. Audio pronunciation for every entry moved to v2.
Six screens
Today
Word of the day, a “Did you know” fact, and a streak counter.
Dictionary
Search 8,550 entries, English to Kwéyòl or back.
Phrases
147 everyday phrases, tap to copy.
Alphabet
All 32 letters, filterable by type, on flip cards with examples.
Grammar
Word order and verb tense markers, with examples.
About
What the project is, and a prompt to rate it.
Through the Ansanm™ lens
| The challenge | What we did | Ansanm band |
|---|---|---|
| A language with a living community and no digital tool | Built for the diaspora, in the language they speak at home | Social band, Ansanm Framework |
| No digital dataset to start from | OCR pass on the print dictionary, then structured entries | Digital band, Ansanm Framework |
| Dictionary errors erode trust fast | Two models cross-check each entry; disagreements go to manual review | Cognitive band, Ansanm Framework |
| A tool nobody had built | Shipped in one sitting to prove it could be done | Purpose band, Ansanm Framework |
Results, and what’s next
Results
Live on Chrome and Firefox with 8,550 entries, 147 phrases, and a full alphabet and grammar guide.
What we’d do differently: the fast timeline left no room for user testing before launch. Real use has surfaced formatting inconsistencies from the OCR pass, and a cleanup is queued alongside audio pronunciation.
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