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You will get I will build a RAG AI assistant for your Data such as documents
Project details
I will build a RAG-based AI solution tailored to your documents or knowledge sources, based on the type of output you need. This can include chat with documents, question answering, summarization, structured extraction, or requirement-based analysis such as comply / not comply classification.
The service is designed for clients who want more than a simple chatbot. I build practical retrieval-augmented workflows that connect your documents to an AI system capable of returning relevant, structured, and useful outputs. Depending on the project scope, the solution can include document ingestion, retrieval logic, prompt design, backend/API support, and customized response formats.
This is a good fit for use cases such as internal knowledge assistants, PDF analysis, standards or compliance checking, proposal review, report understanding, and business document workflows. I focus on building solutions that are clear, useful, and aligned with the real output you need rather than using a one-size-fits-all setup.
The service is designed for clients who want more than a simple chatbot. I build practical retrieval-augmented workflows that connect your documents to an AI system capable of returning relevant, structured, and useful outputs. Depending on the project scope, the solution can include document ingestion, retrieval logic, prompt design, backend/API support, and customized response formats.
This is a good fit for use cases such as internal knowledge assistants, PDF analysis, standards or compliance checking, proposal review, report understanding, and business document workflows. I focus on building solutions that are clear, useful, and aligned with the real output you need rather than using a one-size-fits-all setup.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$60
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 10 days |
Number of Revisions | 2 | 3 | 4 |
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
+$10Frequently asked questions
About Youssef
AI Engineer | FastAPI | LLM Apps | Backend Systems
Dokki, Egypt - 5:03 pm local time
I am an AI and software engineer with 5 years of industry experience and a Master’s degree specializing in Computer Vision and Edge AI. I build intelligent software systems that combine practical business value with solid engineering design.
My expertise includes AI-powered applications, backend development, automation workflows, LLM-based systems, computer vision, and performance-aware software engineering. I work with technologies such as Python, FastAPI, PostgreSQL, C/C++, and modern AI frameworks to create solutions that are scalable, maintainable, and production-ready.
I am particularly interested in projects involving AI assistants, RAG systems, backend platforms, intelligent automation, computer vision pipelines, and edge deployment. I bring both product thinking and technical depth to help clients turn ideas into robust systems.
Steps for completing your project
After purchasing the project, send requirements so Youssef can start the project.
Delivery time starts when Youssef receives requirements from you.
Youssef works on your project following the steps below.
Revisions may occur after the delivery date.
Review documents and expected output
I review the provided files, knowledge sources, and required output format to define the most suitable RAG workflow.
Build the RAG workflow
I create the document ingestion, retrieval, and AI analysis pipeline based on your use case, whether it is chat, Q&A, extraction, or structured analysis.