# Omair Afzal — Software architecture portfolio > Public portfolio of Omair Afzal, a senior software architect working on agentic AI, distributed systems, and open source developer infrastructure. Canonical website: https://omairafzal.com/. Content reviewed: 2026-10-23. The portfolio describes architecture and engineering work across ClickGUARD, REWA, Waffle.Coach, Coredirection, and AlayaCare. Additional project experience includes work on The ENTERTAINER app for Emirates. The public record includes retrieval augmented generation (RAG), AI and machine learning systems, full stack engineering, vector search, mobile and backend platforms, and event driven systems. Career descriptions and performance figures are self-published portfolio statements. GitHub counts on the visual page are a dated snapshot from July 29, 2026; use the linked public repositories for current contribution status. ## Selected projects ### REWA Digital Rent & Rewards Platform Full Stack Cross platform architecture for the UAE's digital rent and rewards platform, connecting a Flutter tenant app, Next.js landlord portal, Symfony operations console, and NestJS services across payments, onboarding, leases, rewards, notifications, and identity workflows. - Tenant mobile, landlord, and operations experiences share consistent contracts through a central service layer - Payment orchestration supports cards, bank transfers, direct debit, consent, reminders, and collection exception lifecycles - UAE Pass, Ejari, invitation, lease, and identity journeys are modeled as resumable cross channel workflows - Rewards, vouchers, notifications, and rent records flow through modular services, workers, and a shared schema authority Technologies: Flutter, NestJS, PostgreSQL, Redis / BullMQ, Next.js, Symfony, AWS. [View in the atlas](/#project-rewa-rent-platform) ### Autonomous Enterprise Knowledge Graph AI/ML A self organizing knowledge base that uses autonomous agents to crawl, classify, and link enterprise data sources. Features multi agent orchestration for continuous graph updates, consistency verification, and gap detection across distributed knowledge stores. - Self healing knowledge graph with automatic inconsistency detection and correction - Autonomous research agents that proactively identify and fill knowledge gaps - Hybrid vector graph retrieval enabling complex multi hop reasoning queries - Enterprise grade RBAC with granular collection level permissioning Technologies: OpenClaw, LangChain, Neo4j, Milvus, Python, FastAPI. [View in the atlas](/#project-agentic-knowledge-base) ### OpenClaw Execution Engine AI/ML The core execution engine enabling AI agents to interact with modern web applications reliably. Handles complex DOM interactions, authentication flows, and dynamic content extraction through semantic understanding and anti detection browser orchestration. - Headless browser orchestration with anti detection and fingerprint randomization - Semantic DOM understanding for resilient, self healing selector generation - High performance async runtime built on Rust and Tokio - Plugin architecture supporting custom action handlers and middleware Technologies: Rust, TypeScript, Puppeteer, CDP, Tokio. [View in the atlas](/#project-openclaw-engine) ### Distributed Vector Search Platform Backend A high performance vector search platform engineered for billion scale embeddings. Implements custom HNSW indexing strategies with distributed sharding for sub millisecond query latency across multi modal embedding types. - Distributed index sharding with automatic rebalancing and failover - Real time vector insertion with tunable consistency guarantees - Multi modal embedding support across text, image, and audio - Production grade observability with per collection Prometheus metrics Technologies: Milvus, Go, Kubernetes, gRPC, Prometheus. [View in the atlas](/#project-vector-scale) ### Autonomous Fraud Prevention System AI/ML A real time ad fraud detection system using autonomous agents to simulate user behavior and validate traffic sources. Processes 100K+ events per second with deep learning behavioral analysis and automated threat rule generation. - Real time behavioral analysis using deep learning classification models - Autonomous traffic validation agents simulating genuine user patterns - High throughput event ingestion pipeline handling 100K+ events per second - Automated rule generation that adapts to emerging fraud patterns in real time Technologies: Python, TensorFlow, ClickHouse, Redis, Docker. [View in the atlas](/#project-clickguard-fraud) ## Related resources - [Downloads and formats](/resources): HTML resource hub. - [Portfolio PDF](/documents/omair-afzal-portfolio.pdf): Visual Systems Atlas portfolio. - [Résumé PDF](/documents/omair-afzal-resume.pdf): Concise recruiter document. [Résumé](/resume)