I turn ambiguous business problems into systems that can actually operate.

My background spans enterprise data, high-scale operations, company building, product leadership and hands-on AI delivery. I work best close to the business problem: understand it with decision-makers, prototype the shortest useful path, then turn what works into a production system.

Today, at Application Partner, I work closely with CFO / executive leadership on business problems that benefit from AI and automation — from rapid prototypes to full end-to-end product delivery.

~PLN 3M raised · CCC Group strategic investor · ~14k-driver network · 61 cities + Prague · ~20 people hired · ~10k invoices/month · ~1.5k automated onboardings · 4 manufacturing sites under SAP Master Data responsibility

Operating systems I ship — not slide decks. Screenshots from production, 2026-08-28.

Portfolio and evidence site: governed AI workflows, case studies, developer passport.

Public operating kit for coding agents (Hermes / Claude Code / OpenCode): one living STATE.md, verified DONE.md (SHA + command), VibingDiary, autocheck spots, no secrets in git.

Feedback → triage → isolated agent → reviewable PR (never auto-merge).

~10,000 invoices/month in production. Business problem → prototype with finance leadership → full operating workflow.

The model extracts and classifies; deterministic rules decide what is allowed next. No model writes to the ledger unsupervised.

What it does (product, not internals): ingest documents · classify type/vendor/cost · validate against accounting rules · human approval at money boundaries · route to finance systems · keep an audit trail.

No public URL. Brand redacted. Private source.

The operator talks to the product in language. The assistant asks only what is missing (who / what / when), then turns intent into a booking — not a 12-field form. Suggestion chips, staff calendar, waitlist, and a feedback loop back into the product. Built as invitation-only while the operating loop is proven with real salons.

What is new (product, not stack): language-first operations instead of admin CRUD; the model may propose, the operator confirms; empty fields become questions, not validation errors.

Operator tools I actually run: remote control of coding-agent terminals over SSH, and a local-first agent TUI with command interception and approvals (the model does not get a raw shell).

~10,000 invoices/month in production.

AI-assisted document processing wrapped in deterministic validation, business rules, approvals, routing and finance integrations.

Pattern: the model proposes; the system validates what is allowed to happen next.

~1,500 people processed with minimal manual intervention.

Schema-driven onboarding automation where process state and next actions remain deterministic and LLMs are used only where language flexibility adds value.

Enterprise automation architecture for turning ambiguous intent into validated plans and deterministic execution through reusable capabilities / primitives.

intent → interpretation → plan → validation → controlled execution → telemetry

AI-first feedback-to-change system: captures user context, triages and clusters feedback, prepares engineering context and dispatches an isolated agent that ends in a reviewable pull request rather than an automatic merge.

Technology-enabled same-day delivery platform operated across 61 Polish cities + Prague. I co-founded the company, hired ~20 people, raised ~PLN 3M and secured CCC Group as a strategic investor while owning product/system direction and software-delivery priorities.

Remote control plane for agent-heavy terminal workflows. Lets me monitor and interact with multiple coding-agent terminals running on an always-on machine over SSH.

Local-first coding-agent CLI with command interception, approval boundaries and multiple model backends. Packaged for PyPI.

Embeddable feedback widget used as the client-side part of a feedback → context → AI triage → reviewable change workflow.

Bidirectional MCP bridge connecting coding-agent ecosystems and tool interfaces across different execution environments.

listen → decompose → model → prototype → constrain → build → automate → observe → improve

I use AI aggressively to compress research, prototyping and implementation time. I do not use it as an excuse to remove deterministic controls from consequential workflows.

I deliberately separate:

• ambiguity that benefits from probabilistic models,
• state and rules that should remain deterministic,
• actions that require validation or policy boundaries,
• decisions that should remain human.

I also run rapid 0→1 product experiments to shorten the distance between an idea and market feedback. My personal speed record is roughly 2 hours from idea → working WordPress plugin → product page → live checkout.

Before AI-native product work, I spent years operating real systems at scale:

• Application Partner — AI Developer / product & technology operator; direct work with CFO and executive leadership; business problem discovery, rapid prototypes and end-to-end AI/automation delivery.
• Xpress Delivery (2018–2025) — co-founder / CEO-CVO; product and technology direction, development-team delivery, enterprise customers, fundraising and multi-city operations.
• 2016–2022 — founder/operator of a ~14k-driver logistics network and internal settlement/accounting system.
• 2 Sisters Food Group, UK (2010–2012) — SAP Master Data Specialist with independent responsibility across four manufacturing sites.

In 2022 I was recognised by BRIEF among the 50 Most Creative People in Business.

• Portfolio / case studies: https://theones.io/ai-product-leader
• LinkedIn: https://www.linkedin.com/in/wojciechwiesner/
• Email: wojciech@theones.io

Production/customer repositories remain private where they contain proprietary code or operational details. This profile intentionally highlights systems and tools that best represent how I build.