I take manual routine off the team with automation and AI: I connect systems to each other and build custom tools instead of pricey subscriptions. The result shows up in numbers: man-hours saved, service payments cancelled, processes that run on their own. Delivered end to end: AI agents and bots, parsers and monitoring, high-volume operations, internal dashboards, API integrations. My specialisation is not an industry, it is a type of task: 2 years, 10+ different domains — from marketing agencies to crypto analytics.
I build through an AI pipeline (Claude Code, Codex, MCP): the AI writes the code — I own the problem statement, the architecture, the tests and the acceptance. Solutions run on LLM APIs (Claude, OpenAI, Gemini) + Python + REST APIs and webhooks; prompt work is daily practice — design, prompt chaining, testing model output. A typical internal tool reaches production in days, not sprints.
My specialisation is not an industry but a type of task — that's why I get into a new domain fast: I start by mapping the team's process together with them, and pick the solution to fit it. The clearest example is a SERM agency: they came with no spec, just “look at where it hurts”. We mapped the processes together and closed them stage by stage — lead gen, outreach, the website, a task manager, dashboards, bots — until marketing, sales and operations ran on their own. Over 2 years I've entered 10+ domains this way: marketing and reputation agencies, media buying, crypto analytics, paid communities, sales.
My strengths are autonomy and predictability. I take the task from a conversation and return a written specification, then work in phases: progress is visible from outside and you can stop at any point. My main professional habit, going back to running a 2,500-account fleet, is to verify the outcome after every action instead of trusting that the script worked. And I count the benefit in numbers: person-hours removed, subscriptions cancelled. “Done” for me means “running in production”, not “the code is written”.
The same “trigger → processing → action” workflows I build in custom code — no platform limits and no subscription per step; at volume that is a difference in money. If part of your processes already lives in n8n / Make / Zapier, I'll pick it up, get up to speed in a day or two and tell you what is worth moving into code and what is cheaper to leave as it is.
The numbers below are person-hours and money taken off the team — not the client's revenue. For two cases you don't have to take my word: the “Proof” button opens a Telegram chat with the client, ask them yourself.
Also built: Telegram account management software (a proxy per account, spam-block checks, warm-up); a bot that transcribes meetings and voice messages and extracts tasks from them; personal bots for my own routine — monitoring the status and limits of the AI services I work with; bots for handling dozens of crypto wallets — balances, statistics, analytics.
Whatever the industry, manual hours go into the same places: data is consolidated by hand, repetitive requests are answered by people, incoming documents are read with human eyes, and internal tools sit in a queue behind the product. Below are eight of those places and exactly what I take off them. Each one is marked with the kind of experience I have.
Every project goes through the same path — that is why the result is predictable and the progress is visible from outside.
I automate processes for agencies, businesses and teams: AI agents and bots on LLM APIs, parsers and monitoring, integrations via REST APIs and webhooks with CRMs and databases, internal services and dashboards. Full cycle: process analysis, specification, plan, implementation, tests, production deployment (cases above).
The year and a half I came into development from: automation for 30+ projects, running a fleet of 2,500 accounts with the full anti-detect infrastructure, high-volume registrations, and analytical monitoring of 100+ sources with my first own parsers.