You will get a technical audit of your AI app, RAG system, or agent workflow
Project details
I will review your existing AI app, RAG system, chatbot, AI agent workflow, or AI-powered MVP and help you understand what can be improved.
This audit is useful if you already have something built, but it has issues such as weak answers, slow responses, high token costs, bad retrieval quality, confusing architecture, unstable agent behavior, or unclear next steps.
I can review:
• AI app architecture
• RAG retrieval flow
• Chunking and embedding strategy
• Vector database usage
• Prompt and tool logic
• LangChain / LangGraph implementation
• AI SDK / Vercel AI SDK integration
• OpenAI / Claude / Gemini usage
• PostgreSQL / Prisma / pgvector setup
• Agent workflow design
• API and backend structure
• Token usage and cost risks
• MVP scope and technical complexity
The goal is to give you a clear, practical list of what is working, what is risky, and what should be improved first.
This audit is useful if you already have something built, but it has issues such as weak answers, slow responses, high token costs, bad retrieval quality, confusing architecture, unstable agent behavior, or unclear next steps.
I can review:
• AI app architecture
• RAG retrieval flow
• Chunking and embedding strategy
• Vector database usage
• Prompt and tool logic
• LangChain / LangGraph implementation
• AI SDK / Vercel AI SDK integration
• OpenAI / Claude / Gemini usage
• PostgreSQL / Prisma / pgvector setup
• Agent workflow design
• API and backend structure
• Token usage and cost risks
• MVP scope and technical complexity
The goal is to give you a clear, practical list of what is working, what is risky, and what should be improved first.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Models
BERT, ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$75
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 0 | 1 | 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 - 12:29 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.
Context review
I review your product, current implementation, and the main issue you want to solve.
Architecture review
I check how the AI flow is designed: model provider, prompts, retrieval, tools, backend, database, and integrations.