Case study
How We Built an Integrated Pipeline to Collect and Curate One of the Largest Datasets for AI Models
A technical guide to processing Arabic data from the web, books, and translation for training language and vision-language models
Education
Learning that adapts,
research that understands Arabic
Adaptive learning and research, built for Arabic
What institutions and learners get
A study plan that responds to how the learner is actually doing.
Fekr is built around subject-specific tutors rather than one generic assistant, each shaped around its domain. The plan moves with the learner: exercises scale with performance, and a wrong answer gets an explanation in conversation rather than a grade days later.
Explore FekrCross-referencing that used to take a week of manual work.
Tbyaan searches the Quran, authenticated Hadith, Tafseer commentary and Islamic Q&A datasets together, understanding classical Arabic and religious context rather than matching keywords, and its research agent returns a structured, source-backed draft instead of a list of links.
Explore TbyaanDiacritics restored before the text reaches a learner or a speech engine.
Sadeed reads the grammar and meaning of the whole sentence to put Tashkeel back, which is what lets the same written word be read correctly in context. It is a preprocessing step for text-to-speech, search and language-learning tools, and it runs on a compact model rather than a large one.
Products
Built on Misraj products
0Plus answers the questions an institution asks of its own data; Fekr carries the adaptive learning work; Tbyaan carries sourced, cited research; and Thakaa builds the tech literacy learners need alongside it. Each is a product an institution can adopt on its own.
Resources
Go deeper
Case study
Eqrra Qur'an : An Intelligent Learning Experience and a Unified Development Architecture
Eqrra Qur'an : An Intelligent Learning Experience and a Unified Development Architecture
Blog
Introduction to Reinforcement Learning
in this article we will introduce the main term of the Reinforcement learning and in the following articles we will go deeper in the field and learn a lot of algorithm and Idea.
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