Academic infrastructure ecosystem. OriginalText - anti-plagiarism and AI-text detection: finds translated plagiarism across languages (Uzbek↔Russian↔English). AkademIndex, AkademScholar, AkademID, AkademJS - scientific research, citation metrics, researcher profiles, and journal publication.
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Problem: Uzbek scientific works are not visible in global systems, there is no national citation index, there is no local solution that identifies AI-text, and cross-language plagiarism is not detected. The solution is an ecosystem on a single engine: AslMatn (anti-plagiarism + AI detection, B2C and B2B), AkademBase (data engine), AkademIndex (multilingual search), AkademScholar (metrics), AkademID (ORCID profiles), AkademJS (journal publication).
Uzbek research is invisible in global academic systems and there is no national citation index. Existing plagiarism tools cannot detect text translated from another language. No local solution identifies AI-generated text. Scholarly heritage in Cyrillic script remains unsearchable.
An ecosystem built on a single data engine: AslMatn detects cross-lingual plagiarism (Uzbek↔Russian↔English) and AI-generated text down to percentage and segment level; AkademBase is the core engine; AkademIndex offers multilingual search (Cyrillic↔Latin); AkademScholar metrics; AkademID profiles; A
A two-layer model. The commercial layer is AslMatn: B2C (pay per word volume and prepaid packages) and B2B (university promo-code programs and API integration). AkademJS offers annual subscriptions for journals. Commercial revenue funds the academic infrastructure layer.
Primary market: Uzbekistan — students, graduate researchers and PhD candidates, faculty; universities and research institutions; academic journal editorial boards. Expansion direction: the Central Asian academic market operating in Russian and Turkic languages.
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