You will get a custom RAG AI chatbot trained on your document
Project details
You will get a custom RAG (Retrieval-Augmented Generation) AI chatbot trained on your own documents — one that answers questions accurately and cites its sources, instead of making things up.
I'm an AI engineer with 7+ years of experience and 70+ delivered projects across 40+ industries. I build production-ready RAG systems using LangChain / LangGraph, FastAPI, and your choice of LLM (OpenAI, Claude, or Gemini), and I handle everything myself — from understanding your goals, to development, to cloud deployment.
Whether you want a customer-support bot, an internal knowledge search tool, or document Q&A, I'll turn your documents into a reliable assistant. I also work well with clients who are new to AI: I explain things in plain language and guide you through every step.
I'm an AI engineer with 7+ years of experience and 70+ delivered projects across 40+ industries. I build production-ready RAG systems using LangChain / LangGraph, FastAPI, and your choice of LLM (OpenAI, Claude, or Gemini), and I handle everything myself — from understanding your goals, to development, to cloud deployment.
Whether you want a customer-support bot, an internal knowledge search tool, or document Q&A, I'll turn your documents into a reliable assistant. I also work well with clients who are new to AI: I explain things in plain language and guide you through every step.
AI Algorithms
AdaBoost, AlexNet, Autoencoder, Convolutional Neural Network, Gated Recurrent Unit, Generative Adversarial Network, Large Language Model, Recurrent Neural Network, Transformer Model, YOLOAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Generated Code, Anomaly Detection, Automatic Speech Recognition, Image Analysis, Image Processing, Image Upscaling, Object Detection, Time Series AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, Microsoft 365 Copilot, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$200
|
Standard
$600
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 1 | 2 | 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 | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $200
Additional Revision
+$70Frequently asked questions
About Takayuki
Data Scientist & AI/Generative AI Developer
Katsushika, Japan - 2:34 am local time
What I do best:
RAG & LLM applications — RAG and GraphRAG systems with LangChain / LangGraph, FastAPI, and AWS Bedrock (Claude). Example: a company-research tool that returns 25+ structured fields in about 30 seconds using async processing.
Document & image AI — OCR and document understanding with GPT-4 Vision (e.g., extracting 20+ listings from a single real-estate document), plus anomaly detection, object counting, and image classification.
Practical deployment — FastAPI services built for the real world, with logging, IP allow-listing, and security headers, deployed on AWS, Azure, or GCP.
Multi-provider flexibility — comfortable across OpenAI, Claude / Bedrock, Gemini, and Perplexity, choosing the right model for the job and budget.
I work well even with clients who are new to AI: I can run the whole process solo, explain things in plain language, and turn a rough idea into a working result. I have a graduate background in mathematics, so I'm at home with the logic and detail behind these systems.
If you have a project in mind — or just an idea you're not sure is possible — feel free to message me. I'd be glad to help.
Steps for completing your project
After purchasing the project, send requirements so Takayuki can start the project.
Delivery time starts when Takayuki receives requirements from you.
Takayuki works on your project following the steps below.
Revisions may occur after the delivery date.
I build and tune your RAG chatbot
I process your documents, build the retrieval pipeline and LLM integration, and tune the system for accurate, source-cited answers.
Process and index your documents
I clean your documents, split them into chunks, and build a searchable vector index so the chatbot can retrieve the right information.