You will get a technical consultation on your AI MVP, RAG, or agent architecture
Project details
will help you plan the technical direction for your AI product, RAG system, chatbot, AI agent workflow, or automation platform.
This consultation is useful if you have an idea, but are not sure what to build first, which stack to use, how complex the MVP should be, or whether you need RAG, agents, or a simpler AI integration.
We can discuss:
• AI MVP architecture
• RAG system design
• AI agent workflow design
• OpenAI, Claude, or Google Gemini integration
• LangChain vs LangGraph vs custom logic
• Google ADK and agent framework options
• AI SDK / Vercel AI SDK for AI web apps
• PostgreSQL, Prisma, pgvector, ChromaDB, or other vector database options
• Ollama and local LLM options
• n8n vs custom automation logic
• Frontend and backend architecture
• API integrations
• Deployment approach
• Cost, token usage, and maintainability
• What to build first for a realistic MVP
After the call, you will have a clearer technical direction and practical next steps.
This consultation is useful if you have an idea, but are not sure what to build first, which stack to use, how complex the MVP should be, or whether you need RAG, agents, or a simpler AI integration.
We can discuss:
• AI MVP architecture
• RAG system design
• AI agent workflow design
• OpenAI, Claude, or Google Gemini integration
• LangChain vs LangGraph vs custom logic
• Google ADK and agent framework options
• AI SDK / Vercel AI SDK for AI web apps
• PostgreSQL, Prisma, pgvector, ChromaDB, or other vector database options
• Ollama and local LLM options
• n8n vs custom automation logic
• Frontend and backend architecture
• API integrations
• Deployment approach
• Cost, token usage, and maintainability
• What to build first for a realistic MVP
After the call, you will have a clearer technical direction and practical next steps.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
BERT, ChatGPT, GPT-4, LLaMA, Midjourney AIWhat's included
| Service Tiers |
Starter
$40
|
Standard
$75
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 1 day | 1 day | 2 days |
Number of Revisions | 0 | 0 | 1 |
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 | - | - | - |
Frequently asked questions
About Sergey
Full-Stack SaaS & AI Developer | Next.js, Python, PostgreSQL
Ukraine - 2:03 am 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.
Context review
I review your idea, current stack, product goal, or existing notes before the call.
Call
We discuss the architecture, stack, scope, risks, and realistic first version.