Across its projects, MISRAJ has consistently sought to connect artificial intelligence with initiatives that create meaningful impact - whether by improving people's lives, work, and productivity or by supporting them in their faith. This case study presents one such initiative: Eqrra Qur'an, an ecosystem that brings together trusted Qur'anic content, user experience, and AI specialized in recitation within a single, scalable architecture.

Prepared by: MISRAJ  |  July 2026

 

Executive Summary

MISRAJ developed Eqrra Qur'an as an integrated, practical model for serving the Holy Qur'an. It combines reading, listening, reflection, and memorization, while adding a specialized AI layer for analyzing recitation and diagnosing errors. Rather than building the product on disconnected sources and inconsistent formats, the project was built on Qurani.ai, a data and API infrastructure that unifies Qur'anic resources and makes them reusable across other applications.

The project addressed two related challenges: the fragmentation of digital Qur'anic content and the technical difficulty of integrating it, and the limitations of general-purpose speech recognition systems in understanding tajwid rules and points of articulation. The solution was therefore built as three interconnected layers: a broad Qur'anic knowledge base, a specialized recitation-correction model, and a user experience that turns these capabilities into everyday tools for readers and learners.

This outcome was the result of a journey that was not without challenges. Among them were:

1. Fragmented Sources and Slow Development

Digital Qur'anic content is distributed across multiple sources, formats, and interfaces. Developers typically have to collect texts, tafsir works, recordings, and linguistic data; standardize and verify them; and build relationships among them before they can begin developing the product experience itself. This process consumes considerable time, increases the risk of inconsistency, and makes advanced features such as semantic search or audio-to-ayah synchronization harder to implement.

2. Word Recognition Alone Is Not Enough to Evaluate Recitation

General-purpose speech recognition systems are designed primarily to convert speech into text. They do not necessarily diagnose the duration of madd, the timing of ghunnah, qalqalah, or tafkhim and tarqiq. In Qur'anic education, the system must understand the details of pronunciation and tajwid rules, while the program or application must provide feedback the user can understand.

3. Turning Technical Complexity into a Calm Experience

Users should not feel the complexity of databases or speech models. The design challenge was to deliver reading, listening, memorization, search, tafsir, and correction in an experience that feels close to a printed mushaf: fast, simple, and dependable for daily use.

The Solution We Built at MISRAJ

The solution was designed as a three-layer ecosystem in which each component supports the others:

1. Qurani.ai - the data and development layer: A unified interface that collects and reorganizes Qur'anic resources and provides access through APIs and the MCP protocol, with capabilities such as flexible search and semantic search.

2. Nabr (nabrquran.ai)  - the recitation-correction model and intelligence layer: A specialized speech engine that analyzes the user's recitation and measures fine-grained acoustic features to diagnose errors and provide immediate correction.

3. Eqrra Qur'an(qurane.com)  - the experience layer: An application that transforms data and models into a practical journey for reading, listening, reflection, memorization, and recitation improvement.

How We Implemented the Project

First, We Unified the Content

We collected textual, audio, and linguistic resources and reorganized them within an interconnected architecture. The platform does not present each ayah merely as standalone text; it links the ayah to tafsir, audio, words, roots, morphological and syntactic tags, tajwid rules, and similar passages. These relationships enabled capabilities such as synchronized listening, meaning-based search, and studying a word in context.

Source: Introductory materials provided by the project team. Figures reflect the documented content and architecture as of July 2026.

 

Next, We Built a Specialized Recitation-Correction Model

The team began with the Whisper engine, then reconfigured and trained it to match the acoustic characteristics of Qur'anic recitation. Training used diverse data reflecting different native languages, pronunciation patterns, and real-world recording conditions, rather than relying solely on ideal studio recordings.

Some natural ambient noise was retained in the training data to improve the model's performance in real-world mobile use, with clips reviewed before being admitted to the training set.

What Does the Model Analyze?

Instead of merely matching words, the model decomposes audio into detailed features. According to the project guide, it analyzes eight characteristics for each sound, including:

• Madd (elongation): Measures how long the sound is extended.

• Ghunnah (nasalization): Detects nasal resonance and measures its timing.

• Qalqalah (echoing): Identifies the acoustic rebound of a consonant carrying sukun.

• Tafkhim and tarqiq (emphasis and lightness): Verifies the correct phonetic quality of the letter.

Turning the Technology into a User Experience

Eqrra Qur'an was built as a reference application that demonstrates the ecosystem's capabilities in a real experience. The journey includes reading the Qur'anic text, synchronized ayah-by-ayah listening, access to tafsir, word analysis, searching similar passages, practicing tajwid rules, and finally recording a recitation to receive an evaluation and correction.

The Eqrra Qur'an interface, as shown in the project's introductory presentation.

Key Experience Capabilities

• Comfortable reading that mirrors the printed mushaf through a simple interface.

• Synchronization with 43 audio editions in an ayah-by-ayah format for precise follow-along.

• Access to multiple tafsir works for each ayah, along with linguistic, morphological, and syntactic resources.

• Fast search across ayahs and similar passages, alongside semantic search by meaning.

• Recitation practice with error analysis and corrective feedback.

 

 

Results and Deliverables

1. Measurable Technical Accuracy

In the tests documented in the project materials, the recitation-correction model achieved a character error rate (CER) of 0.59% and a word error rate (WER) of 2.26%. The model was also compared with established speech systems such as Wav2Vec2, QuartzNet, and Canary.

2. Reusable Architecture

Eqrra Qur'an became more than a final interface; it is a reference application built on Qurani.ai. The platform gives developers access to resources through APIs, an advanced service gateway, and an MCP server that can be integrated with compatible development tools. This turns the effort invested in collecting and organizing data into a reusable technical asset for other Qur'anic products.

3. A Unified Learning Journey

The project brought understanding the text, hearing the recitation, practicing reading, and receiving correction into a single path. This continuity reduces the need to move among disconnected tools and shifts the experience from passive content display to interactive, data-supported learning.

The Impact the Project Was Designed to Achieve

• Enable learners to practice recitation and receive initial feedback at any time from their phones.

• Help Qur'an memorizers review through synchronization, similar passages, and fast search.

• Reduce the burden of collecting and standardizing data for developers of Qur'anic applications.

• Create opportunities for new capabilities such as semantic search, personalized learning, and specialized speech models.

The current materials do not provide actual usage indicators such as user numbers, recitation-session completion rates, before-and-after improvement, or development time saved. This case study therefore reports the technical deliverables and intended impact without attributing unmeasured behavioral or commercial outcomes to the project.

Why Is This a Distinctive MISRAJ Case Study?

Eqrra Qur'an embodies MISRAJ's approach to building AI for specialized domains: begin with the knowledge and data architecture, develop a model suited to the domain, and integrate it into a useful, usable experience. In a sensitive field such as the Holy Qur'an, technical accuracy alone was not enough. The project also required human review, traceability to sources, and a design that preserves the clarity and reliability of the experience.

 

Future Expansion

The roadmap includes official software development kits (SDKs), expanded work on Edge AI and offline capabilities, and open-source support. The project materials also outline a direction for collaboration with the Etqan platform to build an open platform for standardizing recitation recordings under scholarly supervision. The aim is to create open-source data suitable for training mispronunciation detection and diagnosis models. These items are presented as future directions, not completed deliverables.

Case Study Conclusion

MISRAJ transformed Eqrra Qur'an from the concept of a digital mushaf application into an ecosystem that brings together three assets that are difficult to build separately: an interconnected Qur'anic knowledge base, a specialized speech model, and a user experience that connects technology with the daily needs of readers and learners. The result is a foundation that can serve one product today while shortening the path for dozens of Qur'anic applications in the future. Application links are provided below:

Eqrra Qur'an website: https://qurane.com

 

Android app: Eqrra Quran: Read & Memorize on Google Play
iOS app: Eqrra Quran: Read & Hifz on the App Store