Internal process stuck in manual work
Map the real workflow, remove fragile handoffs, and build a tool that fits how the business actually operates.
Michał Gacek / Tiptopdesign
Full-stack AI consulting for business problems that need real execution.
I help founders, product leaders, and technical teams turn complex workflows, product ideas, and AI opportunities into maintainable software that can reach production.
Senior full-stack work delivered across teams at
- BlockyDevs
- Hey Alfie
- Rockwell Automation
- Intercars
- Grin Gaming
- Polette
- Sabre
Problem fit
Useful when the problem is not just "write more code".
The best fit is a situation where business context, architecture, implementation, and AI judgment all have to meet.
AI idea needs feasibility and delivery
Turn an AI concept into a scoped workflow with evaluation, tool-calling boundaries, and production constraints.
Prototype needs production architecture
Move from a promising demo to a system with maintainable boundaries, tests, deployment, and operating discipline.
How I work
Senior execution across the whole path.
Concept clarify the problem and constraints
Architecture choose boundaries, data flows, and trade-offs
Implementation build full-stack features and integration points
Evaluation test quality, AI behavior, and operational risk
Production ship, observe, and iterate with the team
Ways to work together
Four ways to get unstuck - pick the shape that fits this quarter.
Each engagement is sized to do one specific thing well. Start with the smallest one that creates clarity; the rest follows from what we learn.
2-week audit
AI workflow audit
In two weeks you know whether, where, and how AI fits your process - and what shipping it would actually cost.
Best for teams considering AI without a clear hypothesis of where it creates value, or an AI feature stuck in proof-of-concept.
You get
- Process map with candidate AI insertion points
- Per-step feasibility, risk, and quality-evaluation strategy
- Cost estimate covering inference, build, and run
- Go / no-go recommendation with a scoped next phase
1-week review
Technical architecture review
You walk away knowing where the architecture will break, what to fix first, and what is actually fine to leave alone.
Best for pre-investment due diligence, post-MVP reality checks, or onboarding a new technical lead.
You get
- Written architecture review of boundaries, data flows, and integration risks
- Prioritized risk register - what blocks scale versus what is safe to ignore
- 90-day stabilization plan
- Reference architecture for the next 6–12 months
4–12 week build
Prototype to production
A validated demo becomes a system real users can rely on - and that your team can keep extending after I leave.
Best for founders with a working AI or product demo and no production path, and teams that need senior full-stack capacity to cross the gap.
You get
- Production architecture covering data, services, deployment, and observability
- Full-stack implementation in your stack, shipped to real users
- Test and evaluation harness, including AI behavior where relevant
- Handoff documentation and a maintenance runbook
Starts with a 2-week diagnostic
Full-stack product rescue
A stuck project gets a clear path forward - and a senior pair of hands who can execute it alongside your team.
Best for inherited codebases, stalled rewrites, or projects where the original team left or burned out.
You get
- Diagnostic of where delivery is actually blocked - technical, process, or scope
- Triage plan with the next three unblockers, ranked
- Hands-on implementation to ship the next milestone
- Optional ongoing senior capacity once the project is moving again
Proof through work
Technical writing as evidence of judgment.
The blog is a proof library: architecture decisions, AI workflows, testing strategy, backend systems, and interactive explanations.
How I Built an LLM Workflow That Stabilized E2E Tests in a Real Project
Flaky E2E tests kill team velocity. How I turned an LLM from a code generator into a debugger, with skills and anti-flake rules from a real Playwright suite.
When to Use CPQ and the EAV Model - Lessons from Building a Prototype
When CPQ and the EAV model actually make sense, when they are overkill, and why the two so often end up in the same codebase.
Redis in Practice: A Case Study with Hono and TypeScript
A practical case study of Redis in a Hono + TypeScript API - caching, counters, rate limiting, and short-lived state - solving real backend problems.
Interactive lab
Concepts should be touchable when timing, state, or trade-offs matter.
The Redis case study includes a cache-aside playground so readers can feel the difference between a cache miss, a cache hit, and TTL invalidation.