In 1–2 weeks we map your products/services, processes and data, then pick the AI use cases with the highest odds of real value. The result is a decision and a plan — not another list of ideas.
For companies that know AI matters but haven’t picked the process to change yet.
In 1–2 weeks I check whether the AI you pay for is the best available mix of models and tools — and where the same quality costs less. The result is a list of swaps with percentages, not a deck.
For companies that already run AI in their product or processes and pay real invoices for it.
One department from your company. In 2–4 weeks I test whether AI can automate its work — fully or partially — with an owner, data, human-or-AI approval modes and before/after metrics.
For founders, CTOs and product teams that want real change in delivery, support, sales ops or product.
The agent fails in production, answers hallucinate, costs climb — and the team keeps going in circles. In 1–2 weeks I find the cause and unblock it, with before/after metrics.
For teams that already built something and hit a wall — the problem has a name, progress has stopped.
I set up how your team works with coding agents: from ticket, through code and review, to shipping. Ticket patterns, repo instructions and a pilot on your team’s real tasks — measured.
For product-engineering teams that have the AI tools but use them chaotically.
Regular sparring for people making decisions about AI, product and technology: roadmap and architecture review, tool and vendor assessment. Less hype, more substance.
For founders, CTOs and product leaders — monthly billing, leave anytime.
They have a problem getting from experiments to real change in day-to-day operations. People use ChatGPT or Claude. Teams test Claude Code, Claude Cowork or ChatGPT Codex/Work (the optimistic scenario) or Copilot (the pessimistic one). The board has seen a few demos — everyone was impressed. But daily work looks exactly like before.
AI only starts to matter when it changes one of four things:
from ticket to shipped faster than the team alone
the same work done cheaper, with less manual effort
better data, faster research, less guessing
a small team handling volume that used to need an entire department
A few weeks after the Sprint ends, you have on the table:
You’re not behind — almost every company’s AI is exactly where yours is: at the demo stage. I build with AI daily, and most experiments don’t survive. The difference is that I measure them and kill the losers.
I’ve been building digital products since 1998. I co-created Nokaut.pl and Morizon.pl — from startup to IPO and exit. Today I build AI products and work with agents daily: an agent takes a ticket, writes code, another model reviews it, a human approves the final result. Not a futuristic deck — my regular workday.
In 2–4 weeks we design and build repeatable AI workflows or an agent prototype running on a real process.
In 1–2 weeks we map your products/services, processes and data, then pick the AI use cases with the highest odds of real value.
I set up how your team works with coding agents: from ticket, through code and review, to shipping.
Regular strategic sparring for founders, CTOs and product leaders making decisions about AI, product and technology.
I check whether the AI you already pay for delivers the best available quality — and where the same quality costs less.
I solve the hard AI problems your team can’t crack — in your codebase, with before/after metrics.
Fractional means ownership of decisions, operating cadence and outcomes — at quarter-time or half-time, with an AI-first workflow built into the role.
I lead technology and spend the first 30 days installing an agentic development workflow with the team. One person owns architecture, delivery and the measurement of whether AI actually increases velocity.
I lead product and spend the first 30 days installing a continuous workflow from feedback and analytics to decisions. AI speeds up discovery while the roadmap gets one owner and clear numbers.
It’s for companies with a process worth changing, an owner for it, and the readiness to decide based on the numbers.
No. I can train your team if needed, but the goal isn’t tool knowledge. The goal is changing a specific process, product or way of working.
Depends on scope. Most often I deliver a prototype, a workflow spec, a playbook and an implementation recommendation. I also have trusted partners I can build the production solution with. On bigger projects I can join as an advisor/operator working with your team, bringing hands-on experience in rapidly building production-grade software with AI.
That’s also a result you’re paying for: a “kill” verdict with the data on why — instead of quarters spent dragging a pointless project. The Sprint is capped at 2–4 weeks, doesn’t slide into an open-ended engagement, and the deliverable isn’t a deck — it’s a working workflow and its numbers.
Sometimes, but not immediately and not everywhere. First we define the approval mode: outputs are approved by a human or — where it’s safe — by AI. The bigger the blast radius, the more control, logs and human approval.
Yes — if the project has an owner, a clear problem and room for a fast test. I’m a poor fit for a six-month slide process with no decision.
Usually with an AI Opportunity Map or an Agentic Workflow Sprint. The first gives you a decision, the second a working prototype.
Advisory is sparring: regular sessions and a second pair of eyes, while decisions and accountability stay with you. Fractional CTO/CPO is accountability: I lead technology or product, make the calls and own the outcome — at a fraction of full time. In short: if you need recommendations, pick Advisory; if you need decisions, pick fractional.
Ten yes/no questions. Two minutes. An honest answer: sprint, opportunity map — or not yet.
Check if your process is Sprint-readyThe shortest path: 15 minutes on the calendar. We’ll check whether your process fits a Sprint — and whether it’s worth it at all.
Prefer writing? Describe the area/process/challenge — I reply within 1–2 business days.
