Not another chatbot demo. We start with a working commerce platform—and evolve it into an orchestrated multi-agent system where specialist agents retrieve, collaborate, and act through a governed MCP layer.
Follow Rahul on LinkedIn for build notes, architecture decisions, previews, and the launch announcement.
NEXASTORE / EVOLUTION
IN BUILD
.NET 10 · React 19 · PostgreSQL
RAG · MCP · 5 agents · 5 channels
// EXPERIENCE THE TEACHING FIRST
You do not have to guess whether this learning path is for you. Begin with a focused free course, experience the production-first approach, and then move into the complete paid journey.
TRACK 01
Understand how documents become usable AI knowledge before you tackle advanced RAG, agent workflows, and production data pipelines.
TRACK 02
Learn the boundaries and design principles that support maintainable .NET systems before moving into production microservices and agentic architecture.
// STEP TWO · THE COMPLETE AI/ML COURSE
Loved the free introduction? This is the complete next step. Master the intelligence pipeline and turn complex documents into structured, retrievable, agent-ready knowledge and ML-ready data.
RAG · agents · extraction · ML data
.NET 10 · MCP · orchestration · React
// THE PREMISE
NexaStore already behaves like software that has to survive production. That gives every AI decision a real constraint: data truth, safe actions, latency, failure, observability, and trust.
A modular .NET 10 backend and typed React storefront with the commerce fundamentals already working.
Connect product truth and domain knowledge so the experience can answer with evidence instead of improvisation.
Route each request to the right specialist, then use an MCP layer to expose governed tools without surrendering control.
// COURSE BLUEPRINT
You will see why each component exists, where it belongs, and what changes when AI enters a production architecture.
// THE COMPLETE SYSTEM
The course follows the full route from unstructured source material to source-grounded answers and governed actions across customer and enterprise channels.
40 PDFs across buying guides, policies, support, programs, catalogues, and enterprise knowledge.
Azure AI Document Intelligence extracts text, tables, images, metadata, chunks, and embeddings.
Azure AI Search combines vector and keyword retrieval with indexed chunks, metadata, and rules.
The .NET 10 service understands intent, runs hybrid RAG, applies guardrails, and validates responses.
Specialist agents collaborate through explicit responsibilities and an MCP-powered tool boundary.
React web chat, support portal, internal agent portal, REST API, and Microsoft Teams.
// AGENT ROSTER
Instead of forcing a general-purpose assistant to know and do everything, each agent gets a focused domain, a bounded toolset, and a defined hand-off path.
Recommendations, comparisons, and buying guides.
Troubleshooting, setup, and hardware issues.
Returns, warranty, shipping, cancellation, and price match.
Specifications, compatibility, and product comparisons.
Deployment, security, procurement, and IT management.
// WHY FOLLOW NOW
See the trade-offs before the polished lessons arrive: what belongs in deterministic code, what belongs in retrieval, when an agent earns its place, and how to keep every action auditable.
// LEARN WHILE YOU WAIT
The new course is still taking shape. These current courses build the architecture, AI, and delivery instincts that will make the agentic journey click.
Build document intelligence systems with retrieval, agent workflows, structured extraction, and ML-ready data pipelines.
A production-aware modernization playbook for boundaries, migrations, and disciplined architectural trade-offs.
Go from raw data and model tracking to drift monitoring, containers, serving, and AWS EKS.
Build the vocabulary and mental model for document AI before moving into advanced RAG and agent workflows.
Build a production-grade AI ecommerce system with real vector search and retrieval pipelines.
Design and deploy services with decomposition, API Gateway, events, and cloud deployment strategies.
Build production microservices with Clean Architecture, CQRS, Saga, Outbox, and DDD.
Containerize full-stack applications with multi-stage builds, Compose, networking, and deployment pipelines.
Build enterprise full-stack applications with REST APIs, JWT authentication, and production deployment.
Production-ready full-stack development covering security, state, REST integration, and CI/CD.
Master hooks, Context API, routing, and API integration through a real-world food delivery build.
A free primer on Clean Architecture fundamentals before the complete microservices journey.
Udemy pricing and coupon availability can change. The linked offer page shows the current price before checkout.
45 MIN// PERSONAL AI/ML CAREER MENTORING
Book a focused, paid 45-minute session with Rahul. The conversation is planned around your resume, your questions, your current experience, and the AI/ML role you want to move toward.
Connect and DM “MENTORING” ↗ Paid session · pricing and availability are shared directly before booking.WHAT THE SESSION IS BUILT AROUND
“Forty-five focused minutes can save months of scattered learning.”
// THE NEXT BUILD STARTS HERE
Join 7.5K+ builders following Rahul’s production-first notes. Be there for the design decisions, the hard lessons, and the course launch.
Follow on LinkedIn for the launch ↗