# bargiel.AI — Full Content (for AI / answer engines)

Source: https://bargiel.ai/ · Last updated: 2026-06-03 · Language: English
Person: Bartłomiej Bargiel — Engineering Director & Agile AI Practitioner
Contact: bartlomiej@bargiel.ai · +48 511 310 090 · https://www.linkedin.com/in/b-bargiel
Book a 20-minute call: https://cal.com/bargielai/realitycheck

## Headline

Turn a chaotic R&D team into predictable delivery.

"Most AI projects die in the demo. I take them the last, hardest mile into production: monitored, documented, and honestly reported. No watermelons."

Tagline: I engineer outcomes, not status reports.

## Services — Four ways I take AI the last mile.

### Interim Engineering Director
I run your engineering function until it runs itself, then hand it back stronger than I found it.
Who it's for: Organizations where capable teams keep missing dates because no single person owns shipping end to end. The talent isn't the problem. Accountability is.
What you get: Install velocity, capacity and burn-rate tracking from week one; Lead cross-functional delivery and shield engineers from organizational noise; Enforce an Industrial Definition of Done across every workstream.
Outcome: 90 days to delivery dates that are commitments, not guesses.

### Agile AI Transformation
I move your AI from notebook to production with ROI you can measure.
Who it's for: Teams stuck in PoC purgatory: endless experiments, no production deployment, burning R&D budget.
What you get: Reframe sprints to validate hypotheses, not ship features (Agile for probabilistic systems); Build the MLOps and Quality Gates that block non-compliant releases; Ship one prioritized model to production with monitoring and documentation.
Outcome: 90–120 days to your first priority model live in production, monitored and documented.

### Watermelon Check
An independent read on whether your AI project is really green, or red under a thin green skin.
Who it's for: Boards and investors told a project is “on track” who need verification before the next funding tranche or go-live.
What you get: Interrogate velocity, burn-rate and Definition of Done against reported status; Pressure-test the architecture and delivery claims directly with the team; Deliver a concise written true status with the specific risks being hidden.
Outcome: 5 days to a concise written true status of your project, with the specific hidden risks.

### Vendor Due Diligence
I write the requirements vendors can't wriggle out of, then tell you which offer is real.
Who it's for: Teams about to sign an AI or software vendor on a deck and a demo, with no technical way to compare offers or catch lock-in.
What you get: Translate business goals into a precise, testable requirements spec (the RFP); Score incoming offers against the spec, not the sales pitch; Surface hidden lock-in, integration risk and inflated estimates before you sign.
Outcome: 3–4 weeks to a defensible vendor decision, with the material technical risks in writing.

## Engineering DNA — How I work, in three habits.

I see the red the moment I walk in. Within a week you stop hearing “we're on track.” You start seeing velocity, capacity and burn-rate, and the gap between them and the roadmap. I don't translate bad news into corporate language. If a project is red, you know it's red, with the specific reason and the cost of leaving it that way.

Your engineers ship more as the noise stops at me. I absorb the stakeholder churn, the shifting priorities and the politics, and pass the team one clear set of expectations. They build; I handle the rest. The change you feel is quiet: fewer status meetings, fewer surprises, and work that reaches production instead of dying in review.

“Done” means it's live, monitored and documented. I bring an Industrial Definition of Done from regulated, high-stakes environments. A model isn't finished when the demo impresses. It's finished when it runs in production, survives an EU AI Act review, and someone other than its author can maintain it. That bar is uncomfortable at first. Then it becomes the reason things actually ship.

## Proof — Shipped, not slideware.

Machine vision / Industry 4.0. Challenge: A new MLOps platform had to reach production fast, with a web application teams could actually use to manage and deploy computer vision models. "I owned the web application: the layer where an MLOps platform stops being infrastructure and becomes a product people actually work in." Result: 6 months to a production MLOps platform (MVP): the web app live in users' hands.

Banking / AML & compliance. Challenge: Manual KYC onboarding was slow, expensive and exposed to regulatory risk, with no production-grade platform. "Compliance and speed aren't a trade-off. They're both an engineering problem with the same answer: discipline." Result: −80% / −70% onboarding time and KYC operational cost, greenfield to production.

Fintech / fraud prevention. Challenge: A shared utility-sector data bureau had to return an applicant's risk score in sub-second time at the point of acquisition, pooling payment-default data across competing providers. "Latency targets are a promise to the user. You design for them on day one or you never hit them." Result: < 1s consumer-screening latency, delivered on time.

Voice AI / debt-collection automation. Challenge: Manual, call-center-heavy debt collection had to become an automated, multi-tenant Voice AI SaaS platform, on a tight launch window. "The first, hardest step is turning a manual process into a technical roadmap a team can build." Result: −40% operational cost: a production Voice AI MVP shipped in 4 months.

## Testimonials — The people who shipped with me.

"In the age of AI, cross-skill people are rare. Quick feedback cycles, UX blended with application development and cost control were daily business for Bartłomiej. A reliable partner for any software development endeavour." — Piotr Uhruski, Head of Research and Development Software, Basler AG (https://www.linkedin.com/in/piotr-uhruski-87450a1/)

"He and his team did everything to understand complicated legal requirements and translate them into effective technical solutions. I can wholeheartedly recommend him to anyone looking for a competent, reliable and innovative IT development partner." — Alexander Kuhlmann, COO & Senior Consultant, Lexentra GmbH (https://www.linkedin.com/in/alexfromafc/)

"No matter how complex the issue, he approaches it with calm focus, clear reasoning, and practical solutions that move the project forward. Any organization would be lucky to have such a dependable, solution-oriented team leader." — Nazia Rahman, Product Manager, CRIF (https://www.linkedin.com/in/nazia-rahman-472736b7/)

"For any challenge thrown at us — technical or organisational — he consistently found innovative solutions and maintained a positive attitude. A unique blend of product experience, technical expertise and interpersonal skill." — Jan Aulerich, Key Account Manager, Hanseatic Bank GmbH & Co KG (https://www.linkedin.com/in/jan-aulerich/)

## FAQ

### Services & engagements

**Q: What does Bartłomiej Bargiel do?**
A: He is an Engineering Director and Agile AI Practitioner who takes AI projects from demo to production: monitored, documented and honestly reported. He works as an interim engineering leader and as an independent auditor of AI delivery.

**Q: Who should hire him?**
A: Organizations with capable engineering teams that keep missing dates; teams stuck in AI PoC purgatory; boards and investors who need verification before a funding tranche or go-live; and teams about to sign an AI or software vendor without a technical way to compare offers.

**Q: Which industries and domains has he delivered in?**
A: Regulated, high-stakes environments: banking and AML/KYC compliance, fintech fraud prevention, industrial machine vision (Industry 4.0), and Voice AI SaaS. Recent production work includes a greenfield AML platform, an MLOps platform for computer vision, sub-second fraud scoring, and a multi-tenant Voice AI platform.

**Q: How do we start working together?**
A: Most engagements start with a Watermelon Check: a fixed-scope, 5-day independent read on whether your AI project is really on track. It is the low-threshold first step, and it often becomes the basis for an Interim Engineering Director engagement or an Agile AI Transformation if the project needs fixing. Book a 20-minute call and we scope the right entry point together.

**Q: How fast are results?**
A: Watermelon Check: 5 days. Vendor Due Diligence: 3–4 weeks. Interim Engineering Director: predictable delivery dates within 90 days. Agile AI Transformation: first model in production within 90–120 days.

### The Watermelon Check

**Q: What is a “watermelon” project?**
A: A project reported as “green” (on track) on the outside but “red” (in trouble) on the inside. Bargiel's Watermelon Check delivers a concise written true status in 5 days, exposing the specific risks being hidden.

**Q: What is not included in a Watermelon Check?**
A: I diagnose, I don't fix: fixing is a separate Interim Engineering Director or transformation scope. It is not a full security audit or a penetration test. The read is based on conversations with the team and selected artifacts, not a full repository audit, and it carries no deployment commitments. A 5-day scope surfaces the material risks visible in metrics, artifacts and team interrogation; it is deliberately not exhaustive.

**Q: How is a Watermelon Check different from a full engagement?**
A: A Watermelon Check is a fixed-scope, 5-day diagnosis: it tells you the true status and the hidden risks, but it does not fix them. Fixing is a separate Interim Engineering Director or Agile AI Transformation engagement. The Check is the low-threshold first step, and it often defines the scope of the work that follows.

### Approach & standards

**Q: What makes his approach different?**
A: An Industrial Definition of Done from regulated, high-stakes environments (banking, fintech, Industry 4.0). “Done” is not an impressive demo. It is a model running in production, passing an EU AI Act review, and maintainable by someone other than its author.

**Q: What is an Industrial Definition of Done?**
A: A model is not done at 99% accuracy in a notebook. It is done when it is wrapped in a secure API, documented, monitored for drift, integrated into the production or factory process, and able to pass an EU AI Act review by someone other than its author. It is the bar I bring from regulated, high-stakes delivery.

**Q: How is Agile AI Transformation different from regular Agile?**
A: AI is probabilistic, not deterministic, so sprints are structured to validate hypotheses and manage risk, not to ship features against a rigid plan. I use Agile as a risk-management tool that turns open-ended R&D experiments into a predictable delivery schedule, with a Definition of Done, monitoring and Quality Gates.

**Q: Do you handle EU AI Act compliance?**
A: I handle the engineering side of readiness: logging and traceability, post-deployment monitoring, data lineage, Quality Gates, and human oversight built into the architecture. The legal classification of your system and the formal conformity assessment sit with your lawyer or compliance function. I make sure the architecture technically does what the regulation requires; I do not give legal opinions.

### Working with bargiel.AI

**Q: We already have a CTO or an engineering lead. Do we still need you?**
A: I don't replace them. I install delivery, enforce an Industrial Definition of Done, and hand the function back stronger than I found it. Where a lead already exists, I work with them on the system, not instead of them. A 5-day Watermelon Check is a low-cost way to check before you decide.

**Q: You're one person. What if you're unavailable?**
A: Engagements are time-boxed and documented, with handover built in, so the work survives the engagement, not just me. The Industrial Definition of Done and Quality Gates make delivery reproducible, not locked in one person's head. For continuity I draw on a vetted senior network and can bring in a qualified lead to take the project over.

**Q: Does he work outside Poland?**
A: Yes. Services are delivered worldwide, in English and Polish.

## Contact

Your AI works in the demo. Does it work in production? Whether you need a fixed-scope audit or an interim leader to take command of stalled delivery, I step in and make shipping predictable.

What to expect from a call: No sales pitch: 20 minutes on one concrete bottleneck in your current delivery.; Immediate read: a quick assessment of your team structure, methodology, and where releases stall.; Actionable next step: one thing to fix this week, whether or not we work together.

Book a call: https://cal.com/bargielai/realitycheck · LinkedIn: https://www.linkedin.com/in/b-bargiel · Email: bartlomiej@bargiel.ai · Phone: +48 511 310 090