You will get Production Agentic RAG on Your Documents — AWS + Claude API
Project details
You'll get a production-grade RAG (Retrieval-Augmented Generation) system that lets your team ask questions in plain language and get instant, cited answers from your own documents — deployed entirely within your AWS account.
This is not an n8n template or a Flowise drag-and-drop setup. This is production ready engineered software: async document ingestion via SQS with retry logic and dead-letter queue, semantic search powered by AWS S3 Vectors and OpenAI embeddings, Claude API for streaming answers with confidence scoring, full audit log in PostgreSQL, and a real-time WebSocket UI built in Next.js.
Every answer includes source citations. The system honestly says "I don't know" when confidence is low — instead of hallucinating a confident wrong answer.
Background: 7+ years building high-scale data systems (tens of millions of daily events) at one of Eastern Europe's largest e-commerce platforms. I write real code, not prompts.
This is not an n8n template or a Flowise drag-and-drop setup. This is production ready engineered software: async document ingestion via SQS with retry logic and dead-letter queue, semantic search powered by AWS S3 Vectors and OpenAI embeddings, Claude API for streaming answers with confidence scoring, full audit log in PostgreSQL, and a real-time WebSocket UI built in Next.js.
Every answer includes source citations. The system honestly says "I don't know" when confidence is low — instead of hallucinating a confident wrong answer.
Background: 7+ years building high-scale data systems (tens of millions of daily events) at one of Eastern Europe's largest e-commerce platforms. I write real code, not prompts.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingWhat's included
| Service Tiers |
Starter
$500
|
Standard
$700
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 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 |
About Oleg
Senior Software Engineer (Golang, Node.js, Python) + AI enthusiast
Tbilisi, Georgia - 7:08 pm local time
Recommendations from AWS Engineering Manager and ex-Looker/Google CTO — available on LinkedIn.
Steps for completing your project
After purchasing the project, send requirements so Oleg can start the project.
Delivery time starts when Oleg receives requirements from you.
Oleg works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements
We discuss your documents and requirements, expected questions the system should answer and any integrations needed. This can be done via video call or async chat - whichever works best for you
Architecture document
I deliver a system diagram showing data flow, AWS components, and API structure. You approve before any code is written, ask questions and can ask remake it.