A Partnership for a Smarter, Safer Hajj
Itmam's proposed solution will deliver exceptional value to the Ministry of Environment, Water, and Agriculture. We are committed to full compliance, on-time delivery, technical excellence, and a true partnership.
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The Challenge
The Ministry of Environment, Water, and Agriculture seeks to develop and improve the current monitoring system for slaughterhouses, with a critical focus on the Hajj season. The core objectives are to improve efficiency, ensure compliance with health and Islamic standards, leverage advanced AI technology, and enhance the overall experience for all beneficiaries.
Our Methodology
AI Development
- Data Foundation: We begin by ingesting and cataloging the 10+ TB of video and image data provided by the Ministry.
- Expert-Led Annotation: Our specialists work directly with MEWA's veterinarians and officers in a "human-in-the-loop" process to meticulously annotate the data, ensuring our AI learns from true subject matter experts.
- Strategic Data Split: To ensure robust and unbiased performance, the dataset is strategically partitioned: 70% for Training, 20% for Validation, and 10% for Testing.
Project Management
- Hybrid Approach: We employ a hybrid model that combines structural integrity with flexibility to ensure robust and timely delivery.
- Waterfall for Core: The main project phases (Planning, Development, Launch, Operation) follow a structured, sequential Waterfall process for foundational stability.
- Agile for Use Cases: The implementation of the 40+ AI use cases is managed using an Agile framework, allowing for iterative development and continuous feedback from Ministry stakeholders.
System Architecture & Technology Stack
System Architecture
Our solution adheres to the mandated hybrid Edge-Cloud architecture and includes a recommendation for a more advanced, scalable model.
Technology Stack
We use a modern, reliable, and secure technology stack with no closed-source libraries.
- Front-End: React & Deck.gl
- Back-End: Python (FastAPI)
- AI Development: PyTorch & TensorFlow
- Database: PostgreSQL & TimeScaleDB
- Deployment: Docker & Kubernetes
Interactive AI Use Cases
Our solution is comprised of numerous interconnected AI-powered modules. Click on any use case below to see a visual representation of how it works.
Proven Experience
KACST Smart Gate Sentry
Deployed an advanced AI video analytics system for automated security monitoring. The system leverages YOLOv8 and ByteTrack to detect, track, and count individuals in real-time.
Key Outcomes:- Enhanced security with an indisputable 24/7 event log.
- Provided actionable data for facility usage and optimization.
- Drastically reduced incident review time.
KACST Plant AI Assistant
Developed a specialized knowledge discovery platform to transform technical manuals into an interactive knowledge base using advanced RAG techniques.
Key Outcomes:- Accelerated troubleshooting and reduced equipment downtime.
- Enhanced knowledge transfer for junior technicians.
- Preserved and centralized critical institutional knowledge.
Project Plan & Team
Project Timeline (24 Months)
- 4 Wk Platform Planning & Design
- 8 Wk Platform Development & Testing
- 36 Wk AI Models Implementation
- 4 Wk Launch Phase
- 52 Wk Operation & Technical Support
Key Personnel
A dedicated team of certified professionals will be assigned, meeting all RFP requirements.