13+ years designing enterprise-grade solutions with RAG, LLMs, Azure Cloud, .NET Core & Angular — turning complex data into actionable intelligence.
Sixty seconds on who I am, the problem I keep being hired to fix, and a calculator you can drive yourself.
Move the sliders. The figures recalculate as you drag.
Illustrative only, using published list prices. Savings come from routing simple queries to smaller models, caching repeats and trimming prompt bloat — the three levers a gateway makes possible.
I build intelligent, scalable, and predictive systems — designing advanced AI architectures using RAG, LLMs, Predictive AI, Sentiment Analysis, and AI Foundry platforms.
My approach combines microservices, event-driven design, and AI-powered automation to deliver enterprise-scale, cloud-native solutions. I excel in end-to-end software delivery — from solution architecture and API integration to CI/CD pipelines, cloud infrastructure, and automated workflows.
RAG, LLMs, Predictive AI, Sentiment Analysis & AI Foundry
Data Fabric, Service Bus, Data Factory, Copilot Studio
End-to-end software delivery & technical leadership
CI/CD, IaC (ARM, Terraform, Bicep), GitHub Codespaces
A comprehensive toolkit built over 13+ years of professional development
The platform I build enterprise AI on day to day.
Each badge shows hands-on years with that technology
Click any stage to walk through a production RAG pipeline — the same architecture behind the platforms below.
Click a stage above to see what it does
Every number below comes from a system that shipped and stayed in production.
Five layers, one governed platform. Select a layer to see what sits inside it and why it matters.
Anyone can list technologies. This is the decision trail behind the platform every AI feature in the company now runs through.
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Work that shipped, teams that built it, and the moments worth keeping.
EDI 834 enrolment had always run through on-premise clearing houses. We took it to SaaS — multi-tenant, HIPAA-aligned, and able to onboard a payer without shipping anyone a server.
Watch the presentationThis was where I stopped being the person writing the code and started being the person answering “why this way?” — architecture standards, impact assessments, HIPAA-aligned design decisions across a healthcare platform. The technical part I could learn. The harder shift was realising my job had become making other engineers effective rather than being the most effective engineer in the room.
On leading the platform team at MTBC (CareCloud), 2016–2018
Real solutions built for real business challenges across AI, Cloud & Enterprise domains
Manual compliance reviews were slow, error-prone and impossible to scale across hundreds of policy documents.
Built an LLM-powered platform that auto-reads documents, flags regulatory gaps and generates structured audit reports — cutting review time by 70%.
Operations teams spent hours manually compiling data into reports, delaying decisions and wasting analyst time.
Delivered an LLM engine that ingests live operational data and auto-generates structured spot reports — reducing effort by 80%.
Employees wasted hours searching through thousands of documents to find answers buried in PDFs, Word files and reports.
Built a RAG-powered chat UI so users simply ask a question and get exact answers from the document library in seconds.
Repetitive internal workflows (approvals, lookups, handoffs) required constant human coordination across multiple systems.
Designed a multi-agent AI system that autonomously plans tasks, calls tools and completes cross-system workflows — freeing teams for high-value work.
Teams only discovered performance bottlenecks after users complained — reactive firefighting was costly and damaging to SLAs.
Built an ML monitoring platform that proactively detects degradation patterns and auto-recommends or applies fixes before users are impacted.
Sales teams spent 60% of their time on unqualified leads and generic outreach, resulting in low conversion and wasted effort.
Engineered an AI pipeline that scores prospects, crafts personalised outreach via LLMs and feeds qualified leads directly into the CRM.
Support teams were overwhelmed with repetitive queries, causing long wait times and high operational cost.
Deployed a RAG + LLM chatbot that handles 70% of queries autonomously, with full context awareness and 24/7 availability.
Business decisions were made on gut feeling — lack of forecasting led to inventory mismatches and missed revenue opportunities.
Built an Azure ML platform that forecasts demand, predicts churn and surfaces customer behaviour patterns in real-time Power BI dashboards.
Password-based logins were a security liability — brute-force attacks, credential sharing and resets created constant risk and friction.
Delivered a facial recognition + liveness detection system for passwordless, phish-proof enterprise authentication.
DHL's manual route planning failed to account for real-time traffic and capacity — drivers followed suboptimal routes, increasing cost and delays.
Architected an AI system that ingests live data and dynamically re-optimises delivery routes — cutting fuel costs and improving on-time delivery.
Customer feedback was scattered across emails, reviews and social media — no way to spot trends or act on dissatisfaction quickly.
Built an NLP pipeline that ingests multi-channel feedback and surfaces real-time sentiment trends in Power BI for immediate action.
Enterprise-grade multi-tenant DMS with role-based access, PDF viewing, email & WhatsApp sharing and audit trails.
Custom URL shortening service with click tracking, analytics dashboard, geographic insights and AdSense integration.
Language barriers blocked effective communication between global teams and users across regions.
Designed an NLP-based automated translation engine supporting multi-language pairs with context-aware accuracy.
Unstructured visual and textual content was impossible to categorise at scale without massive manual effort.
Built a deep learning pipeline that classifies images, converts image sequences to video summaries and categorises text content automatically.
Training food recognition models from scratch required huge labelled datasets and significant compute resources.
Applied transfer learning on pre-trained CNN models to build a high-accuracy food detection and classification system with minimal training data.
Clinicians lacked tools to identify high-risk patients early — diseases were often caught too late from fragmented medical histories.
Developed an ML model that analyses patient medical history to predict disease likelihood, enabling early intervention and preventive care.
AI models lacked transparency and tamper-proof audit trails — enterprises couldn't trust or verify AI-driven decisions.
Investigated and implemented a framework combining blockchain's immutable ledger with AI decision logging to create verifiable, trustworthy AI pipelines.
Hidden relationships between items, users and behaviours were invisible — businesses missed cross-sell and pattern opportunities.
Built an association analysis framework using graph-based algorithms to surface item relationships, behavioural patterns and recommendation signals.
Dirty, inconsistent data across systems caused downstream analytics failures and unreliable business reports.
Built automated ETL pipelines with validation, deduplication and cleansing rules — ensuring analytics and ML models always run on trusted, verified data.
Fraudulent transactions, system failures and security breaches went unnoticed until significant damage had already occurred.
Developed an ML-driven anomaly detection system with continuous monitoring, auto-alerting and self-improving accuracy over time.
Complete POS system with inventory management, barcode support, debt tracking, and detailed sales reporting.
Frontline staff across three countries could not use the internal knowledge tools, which existed only in English.
Built a speech-to-speech assistant handling English, Malay and German — transcribes, answers from the same RAG index and replies in the caller’s own language.
Finance keyed thousands of supplier invoices by hand each month, with typos surfacing only at reconciliation.
Delivered a vision + LLM pipeline that reads any invoice layout, extracts line items to structured JSON and flags mismatches before posting — 92% straight-through processing.
Every learner got the same fixed course, so strong students were bored and struggling ones quietly fell behind.
Built a recommendation engine that scores each answer, models the learner and re-sequences the next module in real time — lifting completion rates by 38%.
Sensor data from thousands of field devices arrived in bursts that overwhelmed the legacy nightly batch job.
Architected an event-driven ingestion hub on Azure Event Hubs and Service Bus, streaming to time-series storage with live dashboards and sub-second alerting.
Legal review of vendor contracts took days per document, and risky clauses were still missed under deadline pressure.
Built an LLM analyser that compares each clause against the approved playbook, scores deviation risk and drafts redline suggestions for the lawyer to accept or reject.
Leadership had no single view of delivery health — status came from slide decks assembled by hand every fortnight.
Built a portfolio dashboard pulling live data from Azure DevOps and Git, surfacing throughput, risk and dependency clashes across six teams without anyone updating a spreadsheet.
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