Adrij Software

16/08/2026

16/08/2026

The rise of Agentic AI is changing enterprise software.

The next generation of AI won't just provide answers it will help execute tasks and workflows.

But autonomous AI needs a strong foundation:

• Trusted data
• Secure access
• Reliable APIs
• Cloud infrastructure
• Monitoring & observability
• Human oversight

The goal isn't simply to build an AI agent.

The goal is to build an AI system that businesses can trust.

At Adrij Software, we help businesses prepare their data, cloud and software infrastructure for AI-powered workflows.

[email protected] | +91 98619 49240 | adrijsoftware.com

11/08/2026

At Adrij Software, we build data engineering solutions focused on reliable pipelines, data platforms, analytics, cloud architecture, and ML/AI workloads.

📧 [email protected]
📱 WhatsApp: +91 98619 49240

08/08/2026

Your 15-Minute CI Wait Could Be Costing More Than You Think.

CI compute is easy to measure.

Developer waiting time isn't.

Consider a simple example:

25 developers × 5 pushes/day × 15 minutes

That's roughly 31 developer-hours every day spent waiting for CI.

Over 20 working days, that's around 625 developer-hours.

The actual cost depends on your team's fully-loaded engineering cost, but the important point is this:

CI isn't just an infrastructure expense. It's a developer productivity expense.

A few things that can make a big difference:

• Run independent jobs in parallel
• Fix dependency and build caching
• Run cheap checks before expensive ones
• Track P50/P90/P99 pipeline duration
• Identify the slowest jobs instead of optimizing everything
• Treat CI performance as an engineering metric

The goal isn't simply to make CI “faster.”

It's to give developers useful feedback as early as possible.

A 15-minute pipeline might look acceptable on a dashboard.

Multiply it across dozens of developers and hundreds of PRs, and the opportunity cost becomes much harder to ignore.

Measure the waiting time. Then decide whether it's worth fixing.

08/08/2026

Three months ago, we made a significant database change:

MongoDB → PostgreSQL.

Was PostgreSQL automatically “better”? No.

It was simply a better fit for the problems we were solving.

What improved:

• Stronger data integrity

• More powerful SQL queries

• Easier reporting and analytics

• Better consistency for relational data

• Simpler application logic in several areas

What got harder:

• Schema changes required more planning

• Some queries had to be rewritten

• Migration testing took significant effort

• The team had to adapt to a different data model

The biggest lesson wasn't “PostgreSQL is better than MongoDB.”

Choose your database based on your data model, access patterns, consistency requirements, and future needs not because a technology is popular.

Three months later, we're happy with the decision.

The right database is the one that makes your system easier to build, operate and evolve.