Narrow tools
A handful of tools for the questions your team asks every day: find an order, check stock, draft an invoice. Not every endpoint of the API.
For companies rolling out Claude or ChatGPT
Your company systems, inside Claude and ChatGPT
You bought Claude or ChatGPT for the team. Now people paste data from the ERP into the chat by hand. A connector lets the assistant look things up and prepare changes in your own systems, on the logins you already have, asking before anything it cannot undo.
- “someone you can always count on”public LinkedIn recommendation
- “a true bias for action”client CTO, on LinkedIn
- 15+ yearsshipping software
- Free call, no pitch deck30 minutes, then you decide
What the connector does
Your existing login
People use their own accounts, so the assistant sees only what each person may see in the system.
Asks before it acts
Anything that cannot be undone, like deleting, refunding or sending, is marked so the assistant asks the person first.
Measured
Usage per person and per tool from the first day, so you see whether it is worth extending.
How it works
Pick three questions
We start from what people ask most often and which system holds the answer.
One to four weeks
The connector on your API or database, deployed where your team works.
Rollout
A short guide for the team and a usage dashboard.
Then more tools
New processes become new tools, on a monthly care plan if you want.
Systems I connect most often
- Your own API or database, including apps built with AI.
- ERPs, CRMs and ticket systems with an API.
- Mailboxes, documents and spreadsheets the team already uses.
Proof
I built an MCP server on top of an existing product’s login and API, measured adoption per client from the first merge, and found the first agent integrations the same week because the dashboard showed them.
An MCP server from first merge to first external users
- The problem
- Users wanted to log time from their AI assistants. The API existed, the agent surface did not.
- What I did
- I set the tool surface, removed destructive tools, routed every operation through the existing REST controllers so an agent gets no wider access than a person, and instrumented adoption per client in PostHog from day one.
- Result
- First external users within weeks. A bot-protection rule that rejected MCP clients by user agent was found and removed the same day because the dashboard showed it.
See also: AI & agents · PostHog & product analytics
An AI coding agent as a team member, under enforced rules
- The problem
- Coding agents produce ten times the output and ten times the mess. A team of four wanted the speed without a slop swamp on main.
- What I did
- I authored the rule set shared by Claude Code and Codex: mechanical checks over written rules, lint and test gates for security, API surface and CSP, agents working on main with hooks, every PR reviewed by a human, AI-assisted content disclosed as such.
- Result
- In 2026 the agent opened about 30% of merged pull requests across two products and I reviewed about 85% of them. My own output went from under 1,000 to 5,000+ commits a year.
See also: AI & agents · Full-stack & custom software
Agentic content platform built from scratch, quality measured not assumed
- The problem
- Thousands of articles were needed, with AI disclosure built in and without publishing slop.
- What I did
- Late 2024 I built the agent driver: enforced stop conditions, tool-call limits, parallelization, delegation chains. Quality came from best-of-n, LLM-as-judge, scoring against fixtures and plagiarism checks, with many models evaluated across their releases.
- Result
- Thousands of articles shipped through the platform. Content deployed automatically across 300+ generated sites with git-backed bulk operations and reputation monitoring.
See also: AI & agents · AI in your company
Your company systems, inside Claude and ChatGPT
An MCP server on top of your ERP, CRM or your own system and its existing login, with narrow tools that ask before anything irreversible, deployed for your team in Claude or ChatGPT and measured per person.
FAQ
Questions
Does it work with Claude Team or Enterprise and ChatGPT?
Yes. MCP connectors are supported by Claude and by ChatGPT; the same server serves both.
Will the assistant see data a person should not see?
It uses each person’s own login, so it sees what they can see in the system and nothing more.
Can we try it on our API first?
Yes, paste your OpenAPI spec into the free OpenAPI to MCP tools generator to see the proposed tools.
How long does it take?
One to four weeks for the first connector, depending on the number of tools and the login.
Let’s find out in 30 minutes
A free 30-minute call. You bring the problem, I bring questions. No prices on this page because scope comes first.
If I am the wrong person I will say so on the call and point you to someone better.
or write to ciunkos@gmail.com
After you book: a calendar invite, then the call. Nothing else to fill in.
Available from November 2026 · B2B contract or employment · Wrocław, Poland (CET), remote