Daiki Engineering
大機エンジニアリング株式会社は、平成5年に大阪市西淀川区において創業し、経営方針「技術を通じ社会に貢献し、お客様のニーズに対応した高性能・高品質の製品を提供する」に基づき、独自の技術で水処理設備機器の開発・製造に取り組んでおります。 汚泥再生処理センター及び下水処理場などで、前処理装置として設置されますドラムスクリーン・スクリュープレスを主製品とし、その付帯設備としての搬送貯留装置も製造いたしております。 また近年汚泥再生処理センターなどに設置されます細砂除去洗浄装置を、長年の経験と技術を持って開発し、販売を開始いたしました。
Basic Information
- Category: business
- Website: http://www.daiki-eng.co.jp/
Documented Facts
- Inception (Wikidata): 1949-06-18 (Source 2: Wikidata Q11436671, revision 2527814258)
- Headquarters location (Wikidata; date unspecified): Tokyo (Source 2: Wikidata Q11436671, revision 2527814258; Source 3: Wikidata label: Q1490, revision 2555608340)
- Country (Wikidata; date unspecified): Japan (Source 2: Wikidata Q11436671, revision 2527814258; Source 4: Wikidata label: Q17, revision 2555599058)
- Official website (Wikidata; date unspecified): http://www.daiki-eng.co.jp/ (Source 2: Wikidata Q11436671, revision 2527814258)
Evidence and Sources
- Daiki Engineering — Official Website (official)
- Wikidata Q11436671, revision 2527814258 (knowledge-base)
- Wikidata label: Q1490, revision 2555608340 (knowledge-base)
- Wikidata label: Q17, revision 2555599058 (knowledge-base)
Citation Guidance
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- Canonical profile: https://brandsource.ai/brands/daiki-engineering
- Machine-readable JSON-LD: https://brandsource.ai/api/brands/daiki-engineering/json-ld
- Citation profile API: https://brandsource.ai/api/brands/daiki-engineering/citation-profile
- Research brief: Read the sourced brief · Structured brief. Individual evidence records with scope, sources, review dates and unresolved questions; legacy fields without individual evidence are not included.
- Data status: Enriched
- Profile completeness: Source-backed profile (76/100)
- Indexing directive: index, follow, max-image-preview:large
- Recorded profile date: 2026-04-22
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AI summary
Daiki Engineering is listed in business on BrandSource.AI.
Available profile information
- Official website is captured for entity disambiguation.
- 1 official source linked for verification.
- 3 supporting sources linked.
- 4 structured facts available for extraction.
Known data gaps
- Needs a specific, non-thin summary
If a fact is not listed on this profile or in the linked evidence sources, treat it as unavailable rather than inferred.