Writing
Deep technical articles on building production AI systems — computer vision, real-time IoT, LLM pipelines, and MLOps. Written from 4+ years of shipping AI in production.
Available for Consulting
I take on a select number of project-based consulting engagements per quarter — from architecture reviews and LLM pipeline audits to full production builds.
80+ clients · 4+ years production AI · Remote / Islamabad
How to Deploy a Computer Vision Model to Production
Most CV tutorials end at model training. This guide covers every layer I put in place before any vision model goes live — API design, containerisation, versioning, monitoring, and cost optimisation.
Building Real-Time IoT Systems with BLE and WebSockets: Lessons from 200Hz+ Sensor Streaming
The hardest part of building wearable tech isn't the AI. It's the 200 milliseconds between the sensor and the screen. Four years of lessons from production IoT systems — BLE reconnection, protocol selection, edge preprocessing, and monitoring.
LLM-Powered Real-Time Audio Pipelines: How We Built AI Transcription at Scale
Most developers think the hard part of voice AI is the speech-to-text model. It isn't. The hard part is everything around it — the audio ingestion pipeline, the LLM classification layer, the WebSocket architecture, and the operational infrastructure that keeps it all running under production load.
My Production Deployment Checklist for AI Systems: What I Check Before Every Launch
Every item on this checklist exists because I once shipped without it. Seven layers — crash reporting, analytics, UX feedback, bug tracking, infrastructure monitoring, device fingerprinting, and CDN — that I now run before any AI system goes live.
RAG Architecture in Production: Building a Research Intelligence System with ChromaDB and BM25
Production RAG fails in specific ways the tutorials skip. I built PaperIntel — a research intelligence system with citation-level accuracy — using hybrid retrieval, cross-encoder reranking, and systematic evaluation. This is what the full architecture actually looks like.
Model Quantization for Production: How I Cut Inference Cost by 60% Without Touching Accuracy
Your production AI model is probably 4x bigger than it needs to be. I reduced inference time from 340ms to 91ms and cut monthly cloud costs by 60% using INT8 quantization — without changing a single model layer. Here's the full pipeline.
Monolith to Microservices: How We Achieved 3x Throughput on a Live Production System
Most microservices migrations are driven by architectural fashion rather than specific engineering pain. Ours was driven by a measurable scaling problem. This is the story of migrating a live platform without downtime, what broke in ways we didn't anticipate, and what 3x throughput actually looks like.
YOLOv8 in Production: Building a Multi-Camera CCTV Anomaly Detection System
YOLOv8 benchmarks are well documented. What's not documented is what happens when you process 8 simultaneous CCTV feeds in real time, apply zone-based business rules, and deliver WebSocket alerts under 200ms while keeping false positives low enough that security staff actually trust the system.