You will get Build an AI-Powered PDF OCR & RAG Knowledge Base for Instant Q&A
Project details
I help companies turn scattered documents into a reliable AI-powered knowledge system.
This project focuses on building a Retrieval-Augmented Generation (RAG) solution that allows you to search, ask questions, and extract insights from your internal documents such as PDFs, manuals, policies, or compliance files.
Unlike generic chatbots, this system is grounded in your own data. It uses document ingestion, OCR when needed, intelligent chunking, and modern large language models to ensure accurate, traceable answers instead of hallucinations.
This service is ideal for internal knowledge bases, customer support assistants, compliance search, and document-heavy workflows. I focus on clear scope, clean architecture, and practical delivery—whether you need a cloud-based setup, on-premise solution, or code-only implementation.
You will receive a working, explainable system designed for real-world use, not a demo that breaks once documents grow.
This project focuses on building a Retrieval-Augmented Generation (RAG) solution that allows you to search, ask questions, and extract insights from your internal documents such as PDFs, manuals, policies, or compliance files.
Unlike generic chatbots, this system is grounded in your own data. It uses document ingestion, OCR when needed, intelligent chunking, and modern large language models to ensure accurate, traceable answers instead of hallucinations.
This service is ideal for internal knowledge bases, customer support assistants, compliance search, and document-heavy workflows. I focus on clear scope, clean architecture, and practical delivery—whether you need a cloud-based setup, on-premise solution, or code-only implementation.
You will receive a working, explainable system designed for real-world use, not a demo that breaks once documents grow.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Automatic Speech Recognition, Conversational AI, Image Analysis, Image Processing, Machine Translation, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$299
|
Standard
$899
|
Advanced
$1,999
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 20 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 PDF ingestion
(+ 2 Days)
+$50
Cloud / On-prem deployment
(+ 3 Days)
+$300
Bulk document ingestion
(+ 3 Days)
+$200Frequently asked questions
About Xi
Professional AI Engineer | LLM, RAG, Automation & Full-Stack | NFT
Tainan, Taiwan - 7:41 pm local time
computer vision, automation, and Blockchain & NFT integration. Specialized in LLM and RAG-based
knowledge systems, OCR/NER pipelines, distributed backend services, and production-grade AI
applications, with hands-on experience in smart contracts and Web3 integrations.
Steps for completing your project
After purchasing the project, send requirements so Xi can start the project.
Delivery time starts when Xi receives requirements from you.
Xi works on your project following the steps below.
Revisions may occur after the delivery date.
System design & data review
Review project requirements, documents, language, and deployment preferences. Confirm scope and system architecture before implementation.
Document ingestion & RAG implementation
Process documents (OCR if needed), build embeddings, and implement the RAG pipeline. Integrate with the selected LLM and test answer quality.