Product engineer with a controlled AI pipeline
I run a digital product end to end — from market research to production. It’s not about access to AI, but how it’s assembled into work: a controlled pipeline I direct, where AI does most of the work, covers most of the tasks.
A product starts not with code, but with the market
Before building, I work out who needs the product and why it’s better than what exists — otherwise even great code solves the wrong problem.
Market & competitor review
I study what’s already on the market, at what price, and where the weak spots are, so the product lands on a real need.
Honesty instead of pressure
I find where the market leans on the customer with manipulation, and replace it with transparency: an open price as an advantage, not a concession.
Positioning for the audience
I state who the product is for and why before the first screen — it keeps the interface and the copy in one focus.
Path to the user
I think through where people come from and how they reach payment: the product and the path to it are designed together.
estimatemarket numbers (9 competitors, 42 prices) — an estimate, not a measured fact
I build the product as one system — data, interface, content
Data, logic, interface and copy aren’t separate contractors but one coherent system. So decisions line up across the whole vertical, with no seams to glue.
The whole stack in one pair of hands
One coherent system on modern tech (Next.js, React, TypeScript) — no hand-off between the interface and the server side.
Rules live in the data schema
Who sees what, and what syncs with what, is set in the schema itself, not in comments — you can’t bypass it by accident.
Works offline
The app keeps working without a network: the source of truth is on the device, the server is secondary. That matters where connectivity is shaky.
One source, many editions
I assemble text from a single source into different formats (document, book, summaries) in one voice everywhere, with no manual re-layout.
Security and the law are in the foundation, not a sticker before launch
In a sensitive niche, access, payments and the law can’t be bolted on — they have to be a property of the data from day one.
Reliable sign-in
Several ways into one account, one-time codes and protection against interception — sign-in stays solid.
Payments without provider lock-in
The payment provider switches by configuration, with no code changes; a repeat charge can’t go through twice.
Law by project (GDPR)
Versioned consent, an immutable action log, and full two-way data erasure with no breakage.
On sensitive ground — exact rules, not AI
Sensitive topics are driven by fixed rules and verified sources, not free generation.
Speed without debt
Quality isn’t a clean-up after launch: the checks stand at the gate into the release, and every number on this page is tied to code or git.
A quality gate on every module
Types, style, tests and build pass before production, stopping on the first error.
A quality history you can re-check
Dated audits that aren’t rewritten, and explicit debt tracking. Past quality is open.
Every number checkable
Behind any number is a specific file, git command or audit artifact — you can open it and recompute.
Fast by default
Pages arrive ready; heavy code loads only where interaction needs it.
I assemble AI into a process, not just use it
All this breadth — from market to quality — rests on one coherent process: AI assembled into a controlled pipeline under my direction, so it gives coverage across the vertical and checkability at every step.
A controlled pipeline, not chatting with a model
Each role in the pipeline has a clear brief: what it takes on, and what it must return.
Built-in mutual checking
Each cycle has independent checks (facts · security · voice) and a separate pass whose only job is to try to break the result — so it’s stress-tested, not just glanced over.
Checkable results, not a retelling
Each step returns its result in a fixed, structured form, so the cross-checks can run against it.
Reliable long runs
On failure the process doesn’t redo everything; it resumes from the log at the same point.
What I prove, and what I don’t pass off as fact
The same principle as the case and the proof: the hard is separated from the estimated, and the estimated from what I honestly don’t claim yet.
Proven in code
- Built and verified capabilities: on the flagship that’s 42,510 lines, 25 tables, 66 tests.
- Green CI and passed adversarial audits on every module.
- EPUB epubcheck — 0 messages; card payments live in production.
Marked as estimate
- External market-research numbers (9 competitors, 785 links, 42 prices) — an estimate.
- Currency conversions of the price ranges — at the current rate, not fixed.
Not claimed
- Revenue, customer counts, reviews and before/after — the first cohort has only just started.
- I’ll publish only what I can confirm as fact, and with consent.
Three doors into the evidence
The capabilities rest on a real run. Three doors — each answers a different question.
How to engage
The capabilities packaged into concrete engagements — from a light review to end-to-end delivery.
Открыть →HireBring me onto the team
The same skill as a role: tech lead and AI architect — fractional, interim or full-time.
Открыть →CaseProven in practice
Six linked products in one funnel in 10 days — the capabilities in action, every number checkable.
Открыть →Let’s discuss your task
Tell me what you need to build or put in place — I’ll show what maps onto the ready method and how to check the result. No commitment.