You will get LLM and RAG integration built into your Java backend
Top Rated
Top Rated
Project details
Most people building RAG are Python-first and have never worked in a Spring Boot codebase. Most Java engineers haven't shipped retrieval. If you're running enterprise Java and want AI features, that gap is why this is harder to hire for than it should be.
I've spent 14 years in backend Java — banking, healthcare, cross-border regulatory compliance — and I build and operate a production AI assistant on Java 21 and Spring Boot 3.x. Chunking and embedding pipelines, pgvector with HNSW indexing, three answer routes with source grounding, Spring Security JWT, running on Google Cloud.
That means the decisions I'll make on your project are ones I've already made under real conditions: whether pgvector is enough or you need a dedicated vector store, how chunk size affects both answer quality and token spend, when retrieval helps and when it just adds latency and cost.
What you get is a retrieval feature built inside your existing application, using your existing auth and database — not a separate Python service to deploy and maintain.
Top Rated Plus on Upwork, 100% Job Success, 3,000+ hours delivered.
I've spent 14 years in backend Java — banking, healthcare, cross-border regulatory compliance — and I build and operate a production AI assistant on Java 21 and Spring Boot 3.x. Chunking and embedding pipelines, pgvector with HNSW indexing, three answer routes with source grounding, Spring Security JWT, running on Google Cloud.
That means the decisions I'll make on your project are ones I've already made under real conditions: whether pgvector is enough or you need a dedicated vector store, how chunk size affects both answer quality and token spend, when retrieval helps and when it just adds latency and cost.
What you get is a retrieval feature built inside your existing application, using your existing auth and database — not a separate Python service to deploy and maintain.
Top Rated Plus on Upwork, 100% Job Success, 3,000+ hours delivered.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
JavaAI Tools
GitHub CopilotAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$900
|
Standard
$3,000
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 10 days | 21 days | 35 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | ||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Security review of the AI integration
(+ 5 Days)
+$500
Additional data source or retrieval route
(+ 7 Days)
+$800Frequently asked questions
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VJ
Ventura J.
Dec 13, 2021
Java resource experienced on React, Node etc
About Prasanna
Senior Java & AI Engineer | Spring Boot, Microservices, LLM & RAG
100%
Job Success
Pune, India - 11:19 am local time
What I'm doing now: integrating LLM and RAG features into enterprise Java systems. Most people building RAG are Python-first and have never touched a Spring Boot estate. Most Java engineers haven't shipped retrieval. I've done both.
I built and operate a production AI assistant on Java 21 and Spring Boot 3.x — chunking and embedding pipelines, pgvector with HNSW indexing, three answer routes with source grounding, Spring Security JWT, Testcontainers integration tests, running on GCP App Engine.
Also: Spring Boot secure code review and remediation of security audit findings. OWASP-aligned, including AI-generated code.
Stack: Java 21, Spring Boot 3.x, Spring AI, Spring Security, JPA/Hibernate, PostgreSQL, pgvector, Kafka, AWS, GCP, React, TypeScript, Docker, CI/CD.
100% Job Success, Top Rated Plus, 3,000+ hours delivered.
Steps for completing your project
After purchasing the project, send requirements so Prasanna can start the project.
Delivery time starts when Prasanna receives requirements from you.
Prasanna works on your project following the steps below.
Revisions may occur after the delivery date.
Review and plan
I review your codebase, data and use case, then confirm the approach: retrieval design, chunking strategy, embedding model, and expected token cost. You approve before I build.
Build the retrieval pipeline
Ingestion and chunking with overlap, embedding generation, pgvector storage with HNSW indexing. Tuned against your example questions so retrieval returns what it should.