You will get a production-ready MCP server for your AI agent stack
Project details
I build MCP servers that actually ship to production — not just demos. Real auth, real Docker, real observability. I've built CyberMem (cybermem.dev), a self-hosted AI memory layer running in production on Kubernetes, and the Dolyame SDK reaching 1M+ users. I write TypeScript and Python, know the MCP spec inside out, and deliver with a README your team can actually use on day one.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AIOps, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Tools
GitHub CopilotAI Models
ChatGPT, GPT-3, GPT-4, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$49
|
Standard
$89
|
Advanced
$149
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 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 |
About Mikhail
Agentic AI Engineer | MCP | LangGraph | LLM Infrastructure
Tel Aviv, Israel - 12:23 am local time
10+ years in production engineering. Led a platform team from 1 to 13 engineers. Shipped products to 5M+ users while keeping 99.5–100% crash-free rate in production.
My flagship project is CyberMem — a self-hosted MCP memory platform I designed and shipped end-to-end, used by Claude, GPT, Cursor, Gemini, and Perplexity. It runs on Docker, Kubernetes, and Raspberry Pi with Prometheus/Grafana observability, Traefik zero-trust auth, automated versioned releases via GitHub Actions, and Vitest test suite. 550+ commits, 25 versioned releases. Live at cybermem.dev.
I also built EasyOref: a LangGraph-based multi-step alert agent with MCP tool use, BullMQ queues, RAG enrichment, and Telegram delivery — running live in production.
What I work on:
- MCP server development (memory, API bridges, file gateways, custom tools) — Docker/K8s deployed, schema-driven, with auth, observability, and docs
- LangGraph / LangChain workflows — stateful agents with tool use, conditional branching, human-in-the-loop, rollback
- RAG pipelines — document ingestion, embeddings, vector store, retrieval tuning, clean Q&A API
- MCP client setup — Claude Desktop, Cursor, Windsurf, Perplexity — wired correctly with working auth
- Architecture reviews — for teams building agent systems before committing to a design
Positioning: AI Infra Engineer who ships production systems, not demos.
Stack: TypeScript/Node.js, Python, LangGraph, LangChain, FastMCP, Docker, Kubernetes, Redis, BullMQ, Prometheus, Vitest, GitHub Actions.
Send me a short message with your use case, and I'll tell you the fastest way to ship it.
Portfolio: mikhailkogan.dev
Steps for completing your project
After purchasing the project, send requirements so Mikhail can start the project.
Delivery time starts when Mikhail receives requirements from you.
Mikhail works on your project following the steps below.
Revisions may occur after the delivery date.
Scope & requirements review
I review your use case, confirm tool schemas, and send a brief technical plan before writing any code.
Implementation
Build the MCP server with all agreed tools, auth, packages/containers, and README.