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
- 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 are silent killers of team velocity. Here is how I turned an LLM from a code generator into a participant in a diagnostic process - using local skills, knowledge files, and anti-flake rules grounded in a real Playwright + Qase regression suite.
Redis in Practice: A Case Study with Hono and TypeScript
A practical case study of using Redis in a small Hono + TypeScript API - caching, counters, rate limiting, and short-lived state. Patterns that solve real backend problems, not abstract SET foo bar tutorials.
When to Use CPQ and the EAV Model - Lessons from Building a Prototype
CPQ and EAV are two of the most debated patterns in business systems. Here is when they actually make sense, when they are overkill, and why they so often end up in the same codebase.
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.