You will get a production-ready RAG chatbot integrated into your website
Project details
Your AI chatbot will answer customer questions
instantly - 24/7, no hallucinations, grounded
in your actual documents.
I build production-ready RAG systems that ingest
your PDFs, website content, and docs, then deliver
accurate responses via an embeddable chat widget
on your site.
Recent work: built a multi-agent AI system for an
EdTech platform handling real users in production,
and a RAG-based GitHub Code Analyzer that answers
questions about any codebase from actual source code.
Stack: Python · FastAPI · LangChain · OpenAI/Claude
API · FAISS/ChromaDB · React · Docker
What you get:
• Working RAG pipeline with vector search
• Deployed backend API
• Embeddable chat widget
• Admin panel for document updates
• Documentation + handover call
• Clean code, error handling, logging included
Fixed scope, clear communication, no surprises.
instantly - 24/7, no hallucinations, grounded
in your actual documents.
I build production-ready RAG systems that ingest
your PDFs, website content, and docs, then deliver
accurate responses via an embeddable chat widget
on your site.
Recent work: built a multi-agent AI system for an
EdTech platform handling real users in production,
and a RAG-based GitHub Code Analyzer that answers
questions about any codebase from actual source code.
Stack: Python · FastAPI · LangChain · OpenAI/Claude
API · FAISS/ChromaDB · React · Docker
What you get:
• Working RAG pipeline with vector search
• Deployed backend API
• Embeddable chat widget
• Admin panel for document updates
• Documentation + handover call
• Clean code, error handling, logging included
Fixed scope, clear communication, no surprises.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 21 |
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
+$150 - $500
Additional Revision
+$50Frequently asked questions
About Illia
AI & Machine Learning | AI Bot, Generative AI, LangChain, AI Chatbot
Czestochowa, Poland - 11:15 pm local time
could automate - I build the systems that fix that.
I specialize in production-ready AI backends:
- RAG pipelines that let your team query documents
and codebases with natural language
- Multi-agent workflows that automate complex
business processes end-to-end
- FastAPI backends with real-time LLM streaming
- AI chatbots that understand context, not just keywords
- Full-stack AI features: Next.js + Supabase +
OpenAI integration
Recent work:
- Rewrote an EdTech platform backend with multi-agent
AI grading - handling real users in production
- Built a RAG-based GitHub Code Analyzer: ask any
question about a codebase, get accurate answers
from actual source code
- Developed an AI website generator - text prompt
turns into a ready HTML page via LangChain + Docker
My approach: I take full ownership of the project -
from architecture decisions to deployment. Fixed scope,
clear communication, no surprises.
If you need an AI feature shipped fast without
technical debt - let's talk.
Steps for completing your project
After purchasing the project, send requirements so Illia can start the project.
Delivery time starts when Illia receives requirements from you.
Illia works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Planning
Review your documents and use case, define architecture, agree on tech stack and timeline.
RAG Pipeline Development
Build document ingestion, chunking, embeddings and vector search with retrieval testing.