You will get Generative & Agentic AI / RAG Implementation
Project details
I will design and implement practical Generative AI, RAG, or Agentic AI solutions for your enterprise. This service is ideal for teams wanting secure, scalable AI capabilities integrated into real business workflows—not just isolated demos.
My approach bridges the gap between AI strategy and production-ready engineering. I specialize in Java-centric environments using Spring AI, LangChain, and MCP, ensuring your AI implementation is reliable, performant, and secure.
What’s Included
I focus on end-to-end AI integration: use-case validation, architecture design, retrieval strategy, prompt engineering, and data integration. Whether you are building an intelligent search, an automated assistant, or agentic business workflows, I provide the technical leadership to make it happen.
Why work with me?
With 21+ years of enterprise architecture experience, I bring the rigor needed for production AI. I specialize in Java/Spring Boot, helping you implement AI without rebuilding your stack.
My approach bridges the gap between AI strategy and production-ready engineering. I specialize in Java-centric environments using Spring AI, LangChain, and MCP, ensuring your AI implementation is reliable, performant, and secure.
What’s Included
I focus on end-to-end AI integration: use-case validation, architecture design, retrieval strategy, prompt engineering, and data integration. Whether you are building an intelligent search, an automated assistant, or agentic business workflows, I provide the technical leadership to make it happen.
Why work with me?
With 21+ years of enterprise architecture experience, I bring the rigor needed for production AI. I specialize in Java/Spring Boot, helping you implement AI without rebuilding your stack.
Machine Learning Tools
GitHub Copilot, GPT-3, SonnetWhat's included
| Service Tiers |
Starter
$499
|
Standard
$1,199
|
Advanced
$2,499
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Sabu
Cloud & Java Architect | Hands-on Implementation | Agentic AI | RAG
Bengaluru, India - 4:17 am local time
I specialize in full-lifecycle enterprise engineering:
Architecture & Implementation: Expert-level Java, Spring Boot, and microservices architecture with hands-on coding, refactoring, and performance tuning.
Modernization: Migrating legacy monolithic architectures to cloud-native, high-availability environments on Azure, OpenShift, and Kubernetes.
Generative & Agentic AI: Building production-grade AI solutions using Spring AI, RAG pipelines, function calling, and LangChain to automate business processes natively within your Java ecosystem.
DevOps & Delivery: Designing high-velocity CI/CD pipelines, API governance, and security-hardened release cycles.
Modernized a high-transaction banking system (50K+ daily txns) on OpenShift, cutting infrastructure costs by 30%.
Built a custom RAG-based search system that reduced documentation lookup time by 25% and improved search performance by 60%.
Pioneered DevOps transformations that reduced deployment times by 70% and achieved 85% test coverage.
I am ready to help you architect and build your next high-impact project. Let’s discuss your technical roadmap—send me a message, and let's get started.
Steps for completing your project
After purchasing the project, send requirements so Sabu can start the project.
Delivery time starts when Sabu receives requirements from you.
Sabu works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Goal Alignment
We will start with a kickoff call to define your business use case, identify your core data sources, and determine the technical success criteria (e.g., accuracy, latency).
Architecture & Data Strategy
I will design a secure RAG or agentic workflow tailored to your Java/Spring ecosystem. This includes selecting the optimal vector store, LLM integration, and retrieval strategy.