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Vivek Kumar SinghVivek Kumar Singh
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Generative AI Engineering

I build LLM integrations into production systems: retrieval pipelines, agentic workflows, AI-assisted CRM and support tooling. These articles cover the architecture decisions that matter, the cost maths that gets ignored, and the failure modes that only show up once real users arrive.

The theme running through all of it is that generative AI is a component, not a product. The interesting engineering is in the guardrails, the evaluation, and knowing which parts of a workflow should never be automated.

17 articles by Vivek Kumar Singh

Architecture diagram showing Zoho CRM webhook flowing through middleware to Claude and GPT APIs and back
AI

Integrating Claude and GPT with Zoho CRM: An Architecture

A field-tested architecture for LLM CRM integration: webhooks, middleware, Claude or GPT calls, safe writeback, token cost math, and PII guardrails.

· 9 min read

AI

Agentic AI in the Sales Pipeline: What Works Today

A working definition of agentic AI for sales, the bounded-agent architecture that holds up in production, and where autonomous follow-up still breaks.

· 8 min read

AI

Building AI-Assisted Support with Zoho Desk and LLMs

A tiered architecture for AI customer support on Zoho Desk: RAG-grounded drafts, honest deflection rate math, and tickets you should never automate.

· 8 min read

AI

Agentic AI Systems in Production: What Nobody Warns You About

Multi-step AI agents that call tools, browse the web, write code, and make decisions sound powerful — and they are. They're also deeply unreliable in ways that aren't obvious from the demos. Here's what production deployment of agentic systems actually looks like, including the failures.

· 12 min read

AI

OpenAI API Cost Optimization: How I Cut Inference Costs by 60%

Token costs compound fast at scale. I went from $800/month to $320/month without degrading output quality — through prompt caching, model selection strategy, context compression, batching, and smarter retrieval. Every technique is explained with real numbers.

· 10 min read

AI

Generative AI Enterprise Integration: Patterns That Work at Scale

Consumer AI demos don't prepare you for enterprise integration. Security requirements, data residency, audit logging, role-based access, fallback handling — these are the real engineering challenges. Here are the integration patterns I use when AI goes into a serious production system.

· 13 min read

AI

How I Built an AI Agent That Automated 70% of Our Manual Data Processing

Our team spent 3 hours daily on data extraction, classification, and routing. I built an agentic AI system using function calling, structured output, and orchestrated API workflows that now handles it in minutes. Here's the full architecture and the failure modes I didn't anticipate.

· 11 min read

Automation

RPA vs API Automation: Choosing the Right Tool for Enterprise Workflows

RPA (Robotic Process Automation) and API-native automation both solve repetitive work problems. They are not interchangeable. Here's the decision framework I use with enterprise clients — and the expensive mistakes I've seen from choosing the wrong one.

· 9 min read

AI

Integrating LLMs With Java Spring Boot: A Production-Ready Architecture Guide

Most LLM integration guides assume you're building in Python. This one doesn't. Here's how I integrated OpenAI, Anthropic, and local LLMs into Java Spring Boot microservices — including async processing, error handling, cost controls, and observability.

· 12 min read

AI

Building a Production RAG System: Architecture Decisions That Actually Matter

Retrieval-Augmented Generation sounds simple in demos. In production it's a system design problem: chunking strategy, embedding model selection, vector store choice, re-ranking, and context window management. Here's the architecture I use and why each decision was made.

· 14 min read

Automation

Building a Zero-Downtime CI/CD Pipeline With GitHub Actions and AWS

Zero-downtime deployments sound aspirational until you've watched a production rollout wipe out 2 hours of user sessions. Here's the full CI/CD pipeline architecture I've standardized: build, test, containerize, push to ECR, blue/green deploy on ECS, automatic rollback on health check failure.

· 11 min read

Automation

Workflow Automation That Actually Sticks: My Framework After 10 Enterprise Integrations

Most automation projects fail six months after delivery — not because the automation broke, but because nobody maintained it. Here's the framework I use as an automation engineer to design workflows that teams actually keep running, including how I handle edge cases, monitoring, and handoff.

· 12 min read

AI

Why Most AI Integrations Fail (Two Questions That Prevent It)

Most AI features fail not because the model is bad but because the integration was designed without understanding the model's actual failure modes. Two questions fix most of this before you write a single line.

· 8 min read

AI

I Let AI Write My Unit Tests — Here's What I Kept and What I Deleted

The tests it generated were syntactically correct. Most of them passed. About half of them tested nothing meaningful. The process taught me more about what good tests actually are than three years of writing them manually.

· 8 min read

AI

RAG vs Fine-tuning — The Honest Answer Nobody Actually Gives You

Every opinion online either pushes RAG as the universal answer or claims fine-tuning is always superior. After implementing both in production at real scale, my answer is more boring — and far more useful.

· 10 min read

AI

Prompt Engineering Is Real Engineering (And I'll Die on This Hill)

I've seen 'prompt engineer' used as a punchline. I've also seen a poorly structured prompt turn a $200K AI integration into a liability. The craft is real. The stakes are real.

· 7 min read

AI

I Built an AI Feature in a Week — Here's the Messy, Honest Truth

The prototype took 3 hours. The remaining 4 days were spent on reliability, edge cases, and figuring out why it worked perfectly in testing and completely fell apart for real users.

· 9 min read

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