You will get RAG Pipeline / AI Integration Setup
Project details
I build RAG (Retrieval-Augmented Generation)
pipelines that let your application answer
questions from your own documents — with zero
hallucination and full citations.
Most AI integrations fail because they rely on
the LLM's memory. I build systems where the AI
only answers from your actual data — PDFs, docs,
or databases — and cites exactly where the answer
came from.
10+ years of infrastructure experience at Thomson
Reuters India. I've built and shipped a live AI
SaaS (resumeportfolio.in) and an enterprise RAG
system with semantic retrieval, re-ranking,
caching, and Docker deployment.
You get clean code, documentation, and a working
system — not a tutorial copy-paste.
pipelines that let your application answer
questions from your own documents — with zero
hallucination and full citations.
Most AI integrations fail because they rely on
the LLM's memory. I build systems where the AI
only answers from your actual data — PDFs, docs,
or databases — and cites exactly where the answer
came from.
10+ years of infrastructure experience at Thomson
Reuters India. I've built and shipped a live AI
SaaS (resumeportfolio.in) and an enterprise RAG
system with semantic retrieval, re-ranking,
caching, and Docker deployment.
You get clean code, documentation, and a working
system — not a tutorial copy-paste.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Text-to-Image, AIOps, Natural Language GenerationAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, Microsoft 365 Copilot, ReplitAI Models
ChatGPT, GPT-3, GPT-4, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 2 |
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.
Additional Revision
+$25Frequently asked questions
About Farvez
AI & Cloud Infrastructure Engineer | AWS | MLOps | DevOps | Python
Guntur, India - 1:18 am local time
scalable cloud infrastructure — and increasingly,
AI-powered systems that actually run in production.
10+ years of hands-on experience across AWS,
DevOps, and systems engineering at Thomson Reuters
India. I've automated decommissioning of 2,400+
cloud resources saving $180K/year, managed AWS
Lifecycle Management events (Lambda, RDS, EC2),
and built CI/CD pipelines end-to-end.
Recently, I built and shipped resumeportfolio.in
--> a live AI SaaS product using OpenAI, Next.js,
and Supabase, which puts me in a rare position:
I understand both the infrastructure side AND the
AI application layer.
What I can help you with:
→ AWS infrastructure setup, cost optimization,
and automation
→ MLOps pipelines and AI model deployment
→ CI/CD, Docker, Kubernetes, Terraform
→ Python scripting and cloud automation
→ LangChain / RAG / OpenAI API integrations
I work async, communicate clearly in English,
and deliver on time. Based in India, available
for remote work globally.
Steps for completing your project
After purchasing the project, send requirements so Farvez can start the project.
Delivery time starts when Farvez receives requirements from you.
Farvez works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Requirements
Review your documents, understand use case, confirm tech stack and deployment environment.
Build RAG Pipeline
Implement chunking, embeddings, vector store, retrieval, re-ranking and LLM integration.