Siarhei Rudak — Senior Product Engineer
Solving business pain with outcome-driven solutions — end-to-end ownership
I'm Siarhei — 15+ years shipping B2B software. I help founders and builders take products from idea to production — secure and stable by default, without over-engineering.
I work spec-first: every feature starts as an executable specification — expected behavior written down and turned into tests (BDD with TDD). AI agents do the heavy lifting, but behind quality gates: nothing ships because it looks done — it ships because tests prove it works. One accountable engineer end to end, from cutting the scope to running in production. Nothing extra — only what your product needs to work, and keep working. And this is just the backbone — the full system runs deeper.
01 — What I do
Teach
The optimal path from idea to production: scope cutting, executable specs (BDD with TDD), and judgment by business outcome.
Build
Secure and stable by default, with AI agents: tests, quality gates, no over-engineering.
Guide
Which engineering work pays for itself — and which quietly burns the budget.
02 — Work with me
Have a product that needs to ship?
I take a limited number of consulting engagements.
Work with me04 — FAQ
What should my MVP include?
Less than you think: the one action that delivers your core value, and almost nothing else. The explicit list of what you are not building matters more than the feature list. Login screens, settings pages, and admin panels can usually wait.
How long does it take to build an MVP?
Weeks, not months — if the scope is cut honestly. Time balloons when scope does. The calendar is a scoping decision, not an engineering constant.
Do I need a CTO or a technical co-founder?
Usually not at the start. What you need before real users and real money is accountable engineering judgment — and that can be a senior review or a fractional advisor. A full-time CTO is a scale decision, not a day-one decision.
How much does it cost to build an app with AI?
The tools are cheap; the expensive part is wrong decisions and rework. The real bill comes from building the wrong thing, skipping security, and paying for the loop of fixes that follows. That is why judgment costs more than generation.
I built my app with AI — how do I get it production-ready?
Treat the demo and the product as two different things. The gap between them is security, payment edge cases, data isolation between users, and knowing when something breaks. Close that gap deliberately, and get an independent review before you charge money.
Is it safe to build an app with AI coding tools?
Building with AI coding tools is safe for prototypes and learning, and risky by default the moment real users, data, or payments are involved. The typical failures are boring and predictable: API keys exposed in the code, and missing checks on who is allowed to do what. It becomes safe when production practices are applied from the first day, not added after launch.
How do I know my AI-built app isn't leaking user data?
You don't know — until someone actually checks. Three checks any owner can ask for: who can read the database, where the secret keys live, and what one user's account can see of another's. An independent production-readiness review answers these questions before your users do.
Can I trust AI-generated code?
Trust the system around the code, not the code itself. Executable specs, tests, and quality gates decide what ships: the agent writes, verification judges. I apply zero-trust to AI output the same way I would to any contractor's work — nothing is trusted just because it was produced.
When do AI and no-code tools start costing more than they save?
When every new change starts breaking something old. That fix-loop is the signal to add engineering discipline — specs and tests — not necessarily to abandon the tools. The tools are rarely the problem; building without verification is.
05 — The letters
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