You will get a custom AI chatbot for your documents or website
Project details
You will get a custom AI chatbot that can answer questions from your documents, PDFs, website content, FAQs, or internal knowledge base.
I will build the chatbot using RAG, LLM integration, vector search, and a Python/FastAPI-based backend. Depending on the selected package, the project can include a simple chatbot demo, a web app, source-aware answers, document upload flow, API integration, deployment support, and technical documentation.
This project is useful for businesses that want an AI assistant for customer support, internal document search, policy information, manuals, reports, service guidance, or website knowledge.
My experience includes real-world RAG/document search systems and public-facing website chatbot services, so I focus on building practical AI systems that are reliable, maintainable, and ready to test with real users.
I will build the chatbot using RAG, LLM integration, vector search, and a Python/FastAPI-based backend. Depending on the selected package, the project can include a simple chatbot demo, a web app, source-aware answers, document upload flow, API integration, deployment support, and technical documentation.
This project is useful for businesses that want an AI assistant for customer support, internal document search, policy information, manuals, reports, service guidance, or website knowledge.
My experience includes real-world RAG/document search systems and public-facing website chatbot services, so I focus on building practical AI systems that are reliable, maintainable, and ready to test with real users.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, GPT-Neo, LLaMAWhat's included
| Service Tiers |
Starter
$250
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 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 |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Extra document batch
(+ 2 Days)
+$100Frequently asked questions
About Shokhrukh
AI Automation & Computer Vision Engineer | RAG, YOLO, Edge AI
Incheon, South Korea - 9:38 pm local time
I have production-level experience in computer vision, deep learning, embedded/edge AI, and multimodal AI systems. My work includes CCTV analytics, object detection, fire/smoke detection, intrusion detection, autonomous driving perception, smart city analytics, and real-time edge deployment.
𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘄𝗶𝘁𝗵:
▪ 𝗥𝗔𝗚 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 for documents, PDFs, websites, and internal knowledge bases
▪ 𝗔𝗜 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 using Python, APIs, n8n/Make, OpenAI/LLM tools, and workflow systems
▪ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻 systems using YOLO, OpenCV, RTSP streams, and CCTV cameras
▪ 𝗢𝗯𝗷𝗲𝗰𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, segmentation, tracking, and video analytics
▪ 𝗘𝗱𝗴𝗲 𝗔𝗜 deployment and optimization for real-time inference
𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝘁𝗮𝗰𝗸:
Python, C++, FastAPI, OpenCV, YOLO, PyTorch, TensorRT, ONNX, Docker, Qdrant, vector databases, REST APIs, LLMs, RAG, RTSP, CCTV, Jetson/Edge AI.
I have worked on real-world AI systems, including autonomous driving perception, safety monitoring, smart city analytics, and embedded AI deployment. I focus on building systems that are practical, maintainable, and ready to use.
If you need an AI prototype, MVP, RAG chatbot, computer vision pipeline, or production-ready AI system, I can help you design, build, and deploy it.
Steps for completing your project
After purchasing the project, send requirements so Shokhrukh can start the project.
Delivery time starts when Shokhrukh receives requirements from you.
Shokhrukh works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and source data
I review your documents, website links, sample questions, and project goals to confirm the chatbot scope.
Design the RAG workflow
I define the document ingestion, chunking, vector search, prompt flow, and answer generation structure.