AI Product Engineering: Scalable Multilingual RAG Knowledge Platform
Engineered a billion-scale retrieval system with sub-2s latency and zero-hallucination guardrails.
The Challenge
Digital Aalim is a pioneering digital knowledge platform aiming to democratize access to over 50,000 authentic Islamic volumes. Their vision was to provide a personalized, AI-driven scholarly assistant capable of delivering precise guidance across Urdu, Arabic, and Persian while respecting diverse sectarian nuances.
Core Problem
<p>The client faced a dual-crisis of scale and trust: typical LLMs hallucinate religious rulings, which is unacceptable in high-stakes spiritual contexts. Furthermore, the sheer volume of data—spanning billions of vector embeddings—and the need for 'Maslak' (sect) specific isolation made standard RAG architectures insufficient and prohibitively slow.</p>
Inaccurate religious guidance would not only jeopardize the platform's credibility but could lead to significant community backlash. Without a system that could guarantee doctrinal alignment and verify every claim with a physical book/page reference, the project would remain a high-risk prototype rather than a trusted scholarly tool.
The Solution
EnDevSols engineered a sophisticated Multi-Corpus Vector Retrieval layer. We built a custom routing engine that dynamically directs queries to isolated VectorDB instances based on the user's sect, ensuring zero cross-contamination of doctrinal data. To solve the trust issue, we implemented an 'Evidence-First' RAG pipeline that enforces strict citation requirements at the inference level.
Secret Sauce
Our unique innovation was a proprietary multilingual normalization layer that handles script variations and diacritics across ancient texts, coupled with a 'Strict-Fallback' mechanism. If the system cannot find a high-confidence, source-backed match within the specific sect's corpus, it is programmed to refuse the answer rather than risk a hallucination.
Tech Stack
Key Results
Hard Metrics
- Successfully indexed 50,000+ volumes into a billion-scale vector architecture.
- Achieved <2.0 second end-to-end response time for complex multilingual queries.
- 100% of generated responses now include verifiable book and page-level citations.
Values & Impact
- Drastically increased user trust by providing transparency through primary source references.
- Enabled seamless expansion into institutional modules (Maktab) for seminaries.
- Improved accessibility to classical Islamic literature for non-Arabic speakers.
"EnDevSols didn't just build a chatbot; they built a high-integrity knowledge engine. Their ability to handle the engineering complexity of billion-scale vector search while maintaining strict scholarly accuracy was the turning point for our platform."— Founder & CEO, Digital Aalim
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