Implementing autocomplete suggestions in Japanese using Elasticsearch presents unique challenges compared to English due to the language's lack of whitespace between words and the complexity of its writing system, which includes kanji, hiragana, and katakana. Morphological analysis is necessary to parse Japanese text because words are not clearly delineated, and kanji characters often have multiple readings. The process involves converting input to romaji and handling incomplete or mistyped entries to generate relevant suggestions. Elasticsearch's native suggesters are not ideal for Japanese, so custom analyzers, including multi-field configurations and specialized token filters, are utilized to address these challenges. The implementation strategy involves creating a dedicated index for suggestions, utilizing character filters and tokenizers like kuromoji for morphological analysis, and applying techniques such as edge n-gram filtering and synonym management to handle various input forms and enhance suggestion accuracy. The document concludes with considerations for optimizing suggestion accuracy and user experience by incorporating search history and popularity metrics.