You will get Claude Code set up right: agentic dev pipeline, MCP, skills, workflows
Rising Talent
Rising Talent
Project details
Every team is buying Claude Code seats right now. Almost nobody sets it up right, so it writes code fast and breaks things faster.
I run my whole practice on an agentic pipeline: 8 production AI platforms in 13 months, solo. I also build claude_mate, an open-source Claude Code companion. My setup philosophy is public, in the code.
What you get: Claude Code installed and tuned for your repo. A CLAUDE.md that actually steers the model. Permission rules that stop it from touching what it should not. MCP servers wired to your tools. Custom skills and commands for your recurring work. Hooks that keep CI green. On the top tier I train your team live and leave a playbook.
The difference between paying for AI tools and having AI ship your features is the setup. Tell me your stack and I will tell you what your pipeline should look like.
I run my whole practice on an agentic pipeline: 8 production AI platforms in 13 months, solo. I also build claude_mate, an open-source Claude Code companion. My setup philosophy is public, in the code.
What you get: Claude Code installed and tuned for your repo. A CLAUDE.md that actually steers the model. Permission rules that stop it from touching what it should not. MCP servers wired to your tools. Custom skills and commands for your recurring work. Hooks that keep CI green. On the top tier I train your team live and leave a playbook.
The difference between paying for AI tools and having AI ship your features is the setup. Tell me your stack and I will tell you what your pipeline should look like.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI-Generated CodeAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$99
|
Standard
$400
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 7 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 Volodymyr
Full-Stack Agentic Engineer | Ship End-to-End | 13y Hands-On
Kosice, Slovakia - 6:43 pm local time
Last 13 months: eight production platforms delivered end-to-end as the single engineer, across insurance, fintech, venture capital, legal, travel and luxury retail.
RECENT WORK
• Full tour-management ERP for a confidential tour operator, built solo — Fastify, React, PostgreSQL, ECS Fargate, Terraform. 1,000+ commits, empty repository to UAT in four months.
• Core SaaS engine over long-running AI pipelines for a startup whose funding round depended on shipping on schedule — it shipped on time, and the round closed.
• Schema-driven insurance underwriting portal with an AI-assisted review console. Next.js, Azure Functions, PostgreSQL, Microsoft Entra ID.
• AI-powered due diligence platform for professional investors.
• Event-driven multi-agent system on Firebase — eight specialised AI agents that plan, execute and audit work. In production.
BEFORE THE AGENTIC WORK
13 years as hands-on engineer, solution architect and team lead. At LitiGate I led platform engineering on an enterprise legal-AI platform used by Baker McKenzie, through two architecture migrations — one cut load time from 50 seconds to 1-2 seconds and retained an enterprise client that was about to leave. At Hacken I architected and launched 5+ Web3 security and blockchain analytics platforms.
STACK
Agentic & AI: multi-agent orchestration, tool calling, long-running LLM pipelines, prompt and context engineering, Anthropic and OpenAI APIs, Claude Code, Model Context Protocol (MCP), human-in-the-loop review, guardrails.
Application: TypeScript, Python, React, Next.js, Node.js, Fastify, NestJS, PostgreSQL, Prisma, Firebase, Angular, Vue.
Infrastructure: AWS (ECS Fargate, RDS, Lambda, Cognito, CloudFront), Azure Functions, Terraform, Docker, CI/CD.
I also maintain an open-source project, claude_mate — a hardware companion that monitors live Claude Code sessions. Python daemon, Arduino firmware, MIT licensed. My GitHub is linked on this profile.
I am based in Slovakia and work in CET, so European and UK clients get full working-hour overlap. I invoice B2B through my own company.
If you are deciding whether an agent architecture is the right answer at all, that is a conversation worth having before anyone writes code. Send me what you are building and I will tell you straight.
Steps for completing your project
After purchasing the project, send requirements so Volodymyr can start the project.
Delivery time starts when Volodymyr receives requirements from you.
Volodymyr works on your project following the steps below.
Revisions may occur after the delivery date.
Audit your workflow
I look at how you build today: repo, CI, review flow, pain points. We agree on what the agents should own and where the guardrails are.
Install and wire the pipeline
Claude Code configured for your repo: CLAUDE.md, permissions, MCP servers, custom skills, commands, hooks and CI-safe agentic workflows.