Insights on AI, Software & Cloud Engineering
Practical articles from the Vikgol engineering team — covering Generative AI, LLM development, AWS, web development, and software best practices.
Generative AI Consulting: The Enterprise Adoption Guide for 2026
88% of enterprises use AI. Only 6% see real profit impact. The adoption barriers, the ROI data, and a practical 6-phase framework for 2026.
API-First Development: Why Modern Enterprises Are Building Around APIs in 2026
Design the API contract before writing a single line of code — the 2026 enterprise standard for faster delivery, parallel development, and plug-and-play integrations.
Data Lake vs Data Warehouse vs Data Lakehouse: Which One Does Your Business Need in 2026?
Real 2026 costs, honest trade-offs, and a 5-question framework to choose the right data architecture — lake, warehouse, or lakehouse.
AI-Powered Data Analytics: Benefits for Modern Enterprises in 2026
From reactive dashboards to real-time predictive intelligence — how enterprises are using AI analytics to make decisions in seconds, not weeks.
Building Enterprise AI Agents: Architecture & Best Practices for 2026
The 6-layer architecture that separates production AI agents from demo failures — orchestration, memory, governance, and the mistakes that kill most enterprise agent projects.
LLM Cost Optimisation: How We Reduced GPT-4 Costs by 65%
Redis caching, model routing, prompt compression, and batch processing — how we cut a production AI platform's API bill from $41,800 to $14,620/month.
GCC in India 2026: Why Global Companies Are Building Their Next Engineering Team Here
India now hosts 2,100+ Global Capability Centers employing 2 million professionals. Here's what the GCC boom means for engineering teams and AI talent.
Agentic AI vs Traditional AI: What Every Founder Needs to Know
The AI landscape has shifted. Traditional AI is no longer enough. Here is a practical framework for founders and CTOs on when to use each approach.
RAG vs Fine-Tuning: Which AI Approach is Right for Your Business?
A practical decision framework — when to use RAG, when to fine-tune, and when to combine both for enterprise AI.
What Is Agentic AI — And Why Enterprise Adoption Is Harder Than It Looks
Agentic AI is the most significant architectural change in enterprise software since the move to cloud. Here is what it actually means and where it delivers results.
How Real-Time AI Analytics Is Transforming Customer Experience for Fintech
Fintech customers are unforgiving. Here is how real-time AI pipelines cut onboarding drop-offs by 60% and detect fraud in milliseconds.
Cost-Efficient Jenkins Setup with AWS Spot Instances
Cut your CI/CD infrastructure costs significantly by running Jenkins on AWS Spot Instances — with full failover and auto-recovery.
Achieving High Availability on AWS: Multi-AZ Setup with Terraform
A step-by-step guide to building a resilient, multi-AZ AWS architecture using Terraform — the way we do it in production.
How We Reduced AWS Costs by 60% for a UK Fintech Platform
The exact strategies we used to cut a UK fintech's AWS bill from £70K to £28K — step by step.
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Generative AI Consulting: The Enterprise Adoption Guide for 2026
API-First Development: Why Modern Enterprises Are Building Around APIs in 2026
Data Lake vs Data Warehouse vs Data Lakehouse: Which One Does Your Business Need?
AI-Powered Data Analytics: Benefits for Modern Enterprises in 2026
Building Enterprise AI Agents: Architecture & Best Practices for 2026
LLM Cost Optimisation: How We Reduced GPT-4 Costs by 65%
GCC in India 2026: Why Global Companies Are Building Their Next Engineering Team Here
Agentic AI vs Traditional AI: What Every Founder Needs to Know
RAG vs Fine-Tuning: Which AI Approach is Right for Your Business?
What Is Agentic AI — And Why Enterprise Adoption Is Harder Than It Looks
How Real-Time AI Analytics Is Transforming Fintech
Creating a Cost-Efficient Jenkins Setup with AWS Spot Instances
Achieving High Availability on AWS with Terraform
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