Anurag Kumar

Shared schema or schema-per-tenant: choosing a multi-tenant model

The tenancy model you pick early shapes cost, isolation, and how painful onboarding a large customer will be. A practical look at the three common approaches and when each one earns its keep.

Adding AI to a product you have already shipped

You do not need to rebuild your product to add AI to it. A grounded approach to shipping AI features into an existing system — starting where the value is, and keeping the parts that need to be reliable, reliable.

How to Choose an AI Development Company: A Practical Guide for Enterprises

Choosing an AI development company comes down to whether they build in production, ground AI in your data, and own the outcome — not slide decks. Here is a practical checklist of what to look for, the questions to ask, and the red flags to avoid.

AI Development for Healthcare: Where It Helps, and Where Safety Has to Come First

Healthcare has huge upside for AI and the least tolerance for a wrong answer. The use cases are real; the hard part is safety, privacy and compliance. How to build healthcare AI responsibly, with patients protected.

AI Development for Fintech: High-Value Use Cases and What Makes Them Hard

Fintech is one of the highest-value places to apply AI — and one of the hardest to do well. The use cases are clear; the constraints (regulation, explainability, real-time accuracy, data sensitivity) are what separate a demo from a system a bank can run.

Enterprise AI Security: What Buyers Actually Check (Beyond the Standard Questionnaire)

A standard security review misses the new attack surface AI introduces — prompt injection, data leakage through retrieval, unsafe outputs. Here is what enterprise buyers now check specifically for AI, and how to be ready for it.

In-House vs Outsourced AI Development: How Enterprises Should Actually Decide

In-house and outsourced AI are not opposites — they solve different problems at different stages. An honest framework for deciding, plus the hidden costs of building in-house that rarely make the business case.

Why Enterprise AI Pilots Stall Before Production (and How to Get Past the Pilot Trap)

Most enterprises have more stalled AI pilots than production systems. The demo impresses the room, then the project quietly dies — rarely because of the model. Here is why pilots stall, and how to build the ones that actually ship.

How Much Does AI Development Cost? A 2026 Guide to Budgets and Timelines

AI development cost depends far more on scope, data readiness and integration than on the model itself. This honest guide breaks down what drives the number, realistic timelines, and how to control spend without cutting the parts that matter.

What "production-ready" actually means

"It works" and "it is ready for production" are different claims. The gap between them is where most late-night incidents live. A checklist for the difference.

Build versus buy for internal software

Building software you could have bought wastes money; buying software you should have built traps you. A practical way to tell which situation you are actually in.

Why we write the architecture document first

Most software risk is decided before the first line of code. Here is what goes into the architecture document we write at the start of every project, and why we review it with you before development begins.

Migrating a monolith to microservices without downtime

A staged approach that keeps the business running.

Choosing between Laravel and Node.js for enterprise APIs

A decision framework, not a religious war.

What multi-tenant AI actually costs at 1M+ users

The infrastructure realities behind AI features that scale.