You will get a RAG-powered AI chatbot built on your documents and knowledge base
Project details
I will build an AI assistant that can answer questions using your real data.
This can include documents, PDFs, website content, transcripts, interviews, reports, PostgreSQL data, or a custom knowledge base.
The goal is not to create a generic chatbot. The goal is to build an assistant that can search your data, retrieve relevant context, and give useful answers based on your actual information.
I can work with OpenAI, Claude, Gemini, embeddings, LangChain, LangGraph, pgvector, ChromaDB, PostgreSQL, Ollama, Next.js, Node.js, and Python.
Examples of what I can build:
• Internal knowledge base assistant
• Chatbot over PDFs or documentation
• RAG assistant for support or research
• AI assistant over PostgreSQL data
• Chat interface for your website or internal team
• Private/local RAG setup with Ollama if needed
This can include documents, PDFs, website content, transcripts, interviews, reports, PostgreSQL data, or a custom knowledge base.
The goal is not to create a generic chatbot. The goal is to build an assistant that can search your data, retrieve relevant context, and give useful answers based on your actual information.
I can work with OpenAI, Claude, Gemini, embeddings, LangChain, LangGraph, pgvector, ChromaDB, PostgreSQL, Ollama, Next.js, Node.js, and Python.
Examples of what I can build:
• Internal knowledge base assistant
• Chatbot over PDFs or documentation
• RAG assistant for support or research
• AI assistant over PostgreSQL data
• Chat interface for your website or internal team
• Private/local RAG setup with Ollama if needed
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Image Recognition, Natural Language Generation, Natural Language Understanding, Speech Synthesis, Synthetic Data Generation, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Tools
Hugging Face, NVIDIA AI PlatformAI Models
BERT, ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$500
|
Standard
$900
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 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
+$50Frequently asked questions
About Sergey
Full-Stack SaaS & AI Developer | Next.js, Python, PostgreSQL
Ukraine - 4:48 pm local time
I turn complex architectures into scalable products using TypeScript/Node.js, Next.js, and Python. My background is in high-load data infrastructure — indexers processing 280+ blocks/sec, pipelines for platforms tracking $50B+ in live data — so I bring real engineering rigor to AI and web development, not just API wrappers.
𝗠𝘆 𝗰𝗼𝗿𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲
🤖 AI & Agents — LangGraph, LangChain, RAG pipelines, OpenAI/Claude APIs, Vector DBs (pgvector)
⚙️ Backend — TypeScript, Node.js, Python, FastAPI, PostgreSQL, GraphQL, Docker, Kafka
🎨 Frontend — Next.js, React, Vue, Tailwind
𝗦𝗲𝗹𝗲𝗰𝘁𝗲𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗸
▸ Multi-Agent Automation — LangGraph-powered workflows with tool use and human-in-the-loop validation
▸ Enterprise Data Explorer & RAG — full-stack platform across 35+ networks with an AI assistant grounded in live indexed data
▸ Event-Driven Data Pipelines — high-throughput streaming architecture (Kafka, Docker, REST API)
▸ Self-Custodial Security Products — full-stack apps with GraphQL admin panels and API integrations
I work async, write clean documented code, and ship things that hold up under real load. Happy to take on AI integration, agent development, or a full-stack/SaaS build — from a scoped MVP to production hardening.
Steps for completing your project
After purchasing the project, send requirements so Sergey can start the project.
Delivery time starts when Sergey receives requirements from you.
Sergey works on your project following the steps below.
Revisions may occur after the delivery date.
Data review
We review your data sources and decide what should be searchable by the assistant.
RAG setup
I choose the ingestion, chunking, embedding, vector database, and retrieval approach.