You will get I will audit your AI app, RAG system, chatbot, 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
Feedforward Neural Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Generated Code, AIOps, Conversational AI, Image Processing, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Models
ChatGPT, GPT-3, GPT-4, GPT-J, WhisperWhat's included
| Service Tiers |
Starter
$75
|
Standard
$250
|
Advanced
$600
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 0 | 1 | 2 |
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 AI Developer | Agents, RAG, Next.js, Node.js
Ponta do Sol, Portugal - 1:39 pm local time
Most of my work is at the intersection of full-stack development and AI implementation: LLM agents, RAG systems, internal tools, automation workflows, dashboards, and SaaS MVPs.
I can help with both the technical architecture and the implementation: frontend, backend, database, APIs, AI integrations, embeddings, deployment, and workflow logic.
What I usually work with:
• LLM agents and AI workflow automation
• RAG systems over documents, databases, and knowledge bases
• OpenAI API, Claude, embeddings, prompt logic
• Next.js, React, Node.js, TypeScript, JavaScript
• Python, PostgreSQL, Supabase, Docker
• n8n and custom automation pipelines
• Web3 infrastructure, blockchain data, validators, explorers
A few examples of my work:
• Citizen Web3 Ops Agent Factory — AI agents for operational workflows
• CW3 GitHub Agents — AI agents for engineering and repository workflows
• RAG Validatorinfo(dot)com — an AI assistant working with blockchain validator data
I’m a good fit for projects where AI needs to be more than a demo: connected to a real database, real users, real workflows, and a clear product goal.
If you need someone who can think through the architecture and also build the actual system, I can help.
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.