DEV Community: Niraj Kumar

Designing Enterprise-Grade Role-Based Access Control (RBAC) from Scratch

Niraj Kumar — Thu, 23 Jul 2026 14:18:43 +0000

Authorization is one of those topics that every developer uses but few design from first principles.

Most applications start with simple role checks like:

if (user.role === "admin") {
  // allow
}

Eventually those checks become difficult to maintain as new roles, permissions, tenants, and business rules are introduced.

I recently wrote a comprehensive guide explaining how to design an enterprise-grade RBAC system that's scalable, maintainable, and production-ready.

In this article you'll learn:

Why authentication and authorization should remain separate
Designing permission-first architectures
Database schema for enterprise RBAC
Role assignments and permission inheritance
Backend authorization flow
Permission caching strategies
Multi-tenant authorization
Audit logging for compliance
Performance considerations
Common RBAC mistakes to avoid
RBAC vs ABAC vs ACL

Although the examples use TypeScript concepts, the architecture applies equally to Next.js, Node.js, Java, .NET, Go, and other enterprise platforms.

If you're interested in enterprise software architecture, I hope you find it useful.

👉 Full article:

https://www.nirajkumar.in/blog/designing-enterprise-rbac-system

Building Production-Ready AI Features in Next.js: Beyond the Chatbot

Niraj Kumar — Sun, 19 Jul 2026 08:54:38 +0000

Most AI tutorials teach you how to send a prompt to an LLM and display the response.

That's enough for a demo.

But building AI features that are reliable, secure, scalable, and cost-effective in production is a completely different challenge.

As your application grows, you'll start asking questions like:

Where should AI requests flow within your application?
How do you authenticate and rate-limit AI endpoints?
How can you stream responses to improve user experience?
When should AI tasks run in the background?
How do you reduce token costs using caching and Retrieval-Augmented Generation (RAG)?
What should you monitor once your AI feature is live?

These aren't prompt engineering problems—they're software engineering and system design problems.

What You'll Learn

In this article, I explore practical engineering concepts behind production-ready AI applications built with Next.js, including:

🏗️ Production AI architecture
⚡ Streaming AI responses
📚 Retrieval-Augmented Generation (RAG)
🔄 Background processing for long-running tasks
💾 Caching strategies to reduce latency and cost
🔐 Security and rate limiting
📊 Monitoring and observability

The goal isn't just to integrate AI into an application, but to design systems that remain maintainable and scalable as usage grows.

Whether you're building an AI-powered SaaS platform, document processing workflow, or internal business tool, these architectural patterns can help you build with confidence.

Read the Full Guide

I've published the complete guide on my website, where I go deeper into each concept with architecture diagrams and practical explanations.

👉 https://www.nirajkumar.in/blog/building-production-ready-ai-features-nextjs

I'd love to hear how you're building AI-powered applications and what architectural challenges you've encountered.

How I Built an AI-Powered Compliance Marketplace for the Cannabis Industry

Niraj Kumar — Fri, 17 Jul 2026 04:18:01 +0000

The cannabis industry isn't just another e-commerce business.

Every product, every business, and every transaction must comply with strict regulations that vary by state. Unlike traditional marketplaces, selling a product often requires license verification, lab-tested Certificates of Analysis (COAs), age restrictions, and compliance checks before a listing can even become visible.

Recently, I worked on a marketplace that solved many of these technical challenges. This article focuses on the engineering decisions behind the platform rather than the business itself.

The Challenge

Building a regulated marketplace meant solving problems beyond a typical e-commerce application.

Some of the major challenges included:

Business verification before sellers could access the platform.
License validation for different business types.
Product approval workflows.
AI-assisted Certificate of Analysis (COA) processing.
State-specific compliance rules.
Secure marketplace transactions.
Multi-role dashboards.
Product discovery across verified businesses.

The goal was to build a scalable platform where every product entering the marketplace passed compliance checks before reaching buyers.

Architecture Overview

The platform was designed using a modern full-stack architecture.

Frontend

React
Next.js
TypeScript

Backend

Node.js
REST APIs
PostgreSQL
Prisma ORM

Cloud & Infrastructure

AWS
Object Storage
CDN
Background processing
Secure authentication

AI-Powered COA Processing

One of the most interesting engineering challenges was handling Certificates of Analysis (COAs).

Instead of requiring administrators to manually review every report, AI was used to extract structured information from laboratory documents.

The workflow looked like this:

Business uploads a COA.
AI extracts relevant information.
Important fields are normalized.
Validation rules are applied.
Administrators review extracted data.
Products move into the approval workflow.

This significantly reduced manual processing while improving consistency.

Multi-Step Product Approval

Instead of allowing products to become available immediately, every product followed an approval lifecycle.

Business Registration

↓

License Verification

↓

Account Approval

↓

Product Upload

↓

COA Validation

↓

Administrative Review

↓

Marketplace Listing

↓

Available for Buyers

This workflow ensured that only approved products appeared in the marketplace.

Compliance First

A regulated marketplace requires compliance to be part of the application's architecture.

Some examples included:

State-specific business rules.
Business license validation.
Role-based permissions.
Age-restricted access.
Administrative approval workflows.
Product visibility based on compliance status.

These rules influenced both backend APIs and frontend user experiences.

Lessons Learned

Working on this project reinforced several important engineering principles:

Business rules often become the most complex part of an application.
AI should automate repetitive tasks, not replace human review.
Approval workflows deserve first-class architectural design.
Role-based access control becomes increasingly important as platforms grow.
Clean backend architecture makes evolving compliance rules much easier.

Final Thoughts

This project demonstrated how modern AI techniques, scalable backend architecture, and well-designed workflows can simplify complex compliance processes while providing a better experience for businesses and administrators.

It was an excellent opportunity to combine full-stack engineering with AI-assisted document processing, workflow automation, and scalable SaaS architecture.

Read the Complete Case Study

This article provides a high-level overview of the engineering approach.

The complete case study includes the project architecture, workflow diagrams, technical decisions, challenges, and implementation details.

Read the Complete Case Study

This article is a condensed overview of the engineering challenges and architecture.

👉 Read the full case study on my website

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