You will get Custom RAG AI Agent: Automate Internal Document Workflows with Python
Project details
Manual data extraction and fragmented document analysis cause massive operational drag. Standard AI tools fail here because they hallucinate, ignore private context, and risk data leaks.
I build secure Retrieval-Augmented Generation (RAG) systems and multi-agent workflows that turn your private business data (PDFs, CSVs, legal docs) into actionable intelligence. Your team gets a localized, interactive web dashboard to query data instantly with strict factual accuracy.
The Architecture:
• Python & LangChain: Clean, modular backend code built to automate complex workflows.
• Vector Databases: Scalable storage (Pinecone, ChromaDB, or FAISS) for precise context retrieval.
• UI Integration: Clean web application interface designed for frictionless team adoption.
From a lightweight proof-of-concept script to an advanced multi-agent architecture deployed on AWS/Azure, I deliver secure, scalable, and fully documented code.
Stop wasting hours hunting through files. Let's build a system that automates your operations and cuts down manual overhead. Drop your project requirements, and let's get started.
I build secure Retrieval-Augmented Generation (RAG) systems and multi-agent workflows that turn your private business data (PDFs, CSVs, legal docs) into actionable intelligence. Your team gets a localized, interactive web dashboard to query data instantly with strict factual accuracy.
The Architecture:
• Python & LangChain: Clean, modular backend code built to automate complex workflows.
• Vector Databases: Scalable storage (Pinecone, ChromaDB, or FAISS) for precise context retrieval.
• UI Integration: Clean web application interface designed for frictionless team adoption.
From a lightweight proof-of-concept script to an advanced multi-agent architecture deployed on AWS/Azure, I deliver secure, scalable, and fully documented code.
Stop wasting hours hunting through files. Let's build a system that automates your operations and cuts down manual overhead. Drop your project requirements, and let's get started.
AI Algorithms
Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, AIOps, Conversational AIAI Development Language
PythonAI Tools
Hugging Face, StreamlitAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$350
|
Standard
$1,500
|
Advanced
$3,800
|
|---|---|---|---|
| Delivery Time | 4 days | 12 days | 25 days |
Number of Revisions | 2 | 3 | 5 |
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 |
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
AS
Ahmed S.
Dec 31, 2025
AI Developer / LLM Engineer (Training Models on Large Microservices Codebase)
About Hamid
AI Systems Architect | RAG & Multi-Agent Orchestration | Python + LLMs
Lahore, Pakistan - 8:50 pm local time
If you are a CEO, CTO, or founder looking to replace manual processes with intelligent, scalable architecture, I build the systems that execute those outcomes. I specialize in taking complex AI concepts and deploying them into functional, secure web-based software.
Core Technical Stack:
AI Architecture: Retrieval-Augmented Generation (RAG), Multi-Agent Orchestration, LLM Fine-Tuning/Integration
Backend & Processing: Python, LangChain, Custom API Development
Web Integration: Deploying AI models into custom, full-stack web environments
Recent Implementation Proof:
Currently engineering Wukala-GPT, an enterprise-grade AI system requiring complex document parsing, multi-agent logic, and secure data handling.
Successfully delivered a 5-star rated custom system on Upwork, prioritizing clean code, proactive communication, and zero friction for the client.
I do not just write scripts; I build end-to-end custom web applications that house your AI agents securely. Whether you need a localized RAG system for internal documents or a scalable multi-agent workflow for customer-facing SaaS, I deliver production-ready infrastructure.
Happy to answer any questions about your system architecture or provide a technical roadmap for your project.
Steps for completing your project
After purchasing the project, send requirements so Hamid can start the project.
Delivery time starts when Hamid receives requirements from you.
Hamid works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture & Data Alignment
Reviewing the client requirements, workflows, and sample data. Setting up the data ingestion strategy, chunking logic, and the initial vector database configuration.
RAG Pipeline & Logic Engineering
Building the core Python backend. Integrating LangChain, setting up the custom LLM API connections, embedding pipelines, and refining the semantic search/retrieval accuracy to eliminate hallucinations.