You will get Production-Grade Intelligent RAG Pipeline for Large-Scale Enterprise Data
Project details
You will get a production-grade Intelligent RAG pipelines - cloud-deployable, horizontally scalable systems engineered for large document corpora and real enterprise workloads. Not a proof-of-concept. Not a demo wrapper. A system designed to handle your data at the scale it actually exists.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Regression AnalysisAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Speech, AI-Enhanced Classification, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
GPT-4What's included
| Service Tiers |
Starter
$750
|
Standard
$2,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 14 days | 21 days | 35 days |
Number of Revisions | 2 | 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.
Additional Revision
+$100Frequently asked questions
About Arpit
AI Agents & RAG Engineer|LangGraph, CrewAI, Bedrock|13+ Yrs Data Engg.
Bengaluru, India - 9:59 am local time
On the AI side, I design and ship multi-agent systems, RAG pipelines, and LLM-powered automation using LangChain, LangGraph, and CrewAI. I've architected retrieval systems backed by Elasticsearch and Qdrant, built autonomous agent workflows that replace entire manual processes, and deployed enterprise-grade AI applications end-to-end - from prompt design to production infra. If your team is trying to move from "AI prototype" to "AI product," that's exactly where I operate.
That AI layer runs on solid data infrastructure - and I build that too. On the engineering side, I work across Apache Spark and AWS Glue for large-scale data transformation, with deep hands-on experience across the AWS ecosystem: EMR, EKS, Bedrock, Glue, CloudFormation, CodePipeline, VPCs, and Route53. I architect pipelines that are reliable, observable, and built to grow - not quick fixes that break at scale.
I take on a small number of projects at a time so every engagement gets my full attention. If you're building something meaningful in the AI or data space and need a senior engineer who's done this before - let's talk.
Steps for completing your project
After purchasing the project, send requirements so Arpit can start the project.
Delivery time starts when Arpit receives requirements from you.
Arpit works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Document Onboarding
Collect sample documents, confirm use case, LLM preference, cloud environment details, and expected data volume. Align on scope before writing a single line of code.
Architecture Design & Approval
Share a system architecture diagram covering ingestion pipeline, vector DB, knowledge graph, and retrieval flow. Client reviews and signs off before build begins.