Bartosz Mikulski

Bartosz Mikulski

AI Risk Prevention | I help fintech leaders stop AI hallucinations before they trigger public failures, compliance issues, or customer churn.

Bartosz Mikulski's Picture

Prevent AI Hallucinations in Production

Get a personalized risk audit + 7-day roadmap to eliminate hallucinations from your customer-facing AI systems.

Bartosz Mikulski
14 years programming (5 years of MLOps, 4 years of data engineering) Contributed to "97 Things Every Data Engineer Should Know" Conference speaker, podcast guest 1020 engineers trained

AI Hallucinations Are Business Risks

Most AI systems fail in production due to unseen risks and missing evaluation layers:

Data Drift

Test data doesn’t reflect real-world scenarios

Pattern Recognition Gaps

Teams miss patterns that lead to hallucinations

Missing Domain Expertise

Experts not consulted = critical context missed

Your Zero-Hallucination Game Plan

One call. Seven days. Your roadmap to AI you can trust in production.

🎯 What You Get

  • ✔️ 1-hour AI Risk Audit Call
  • ✔️ System output + architecture evaluation
  • ✔️ Custom 7-day roadmap to eliminate hallucinations
  • ✔️ Risk mitigation summary for stakeholders
  • ✔️ Option for done-for-you implementation

💰 Investment

Starts from: €2,000

Limited availability. High-impact results. Zero fluff.

Proof: Real Results from Real Teams

Production hallucinations cut from 57% to 0.3% in just 4 days.

AI Error Reduction Results Screenshot

Before: 57% error rate
After: 0.3% — achieved in just 4 days.

Why Work With Me?

I help APAC fintech teams prevent hallucinations in production before it impacts their customers or compliance.

  • ✅ 14 years writing high-stakes production systems
  • ✅ MLOps & data engineering expertise
  • ✅ 1020 engineers trained in AI systems
  • ✅ Contributor to "97 Things Every Data Engineer Should Know"

I specialize in supporting engineering teams across fintech hubs like Singapore, Hong Kong, and Jakarta, where precision and trust matter most.

Trusted by Engineering Leaders

"I worked with Bartosz while he was a member of the Data Engineering team at Fandom. He is very professional and open to share his knowledge with his teammates and beyond. His approach was always very data-driven and he has great knowledge in the Data Engineering area, which made him a very valuable partner in discussions."
Martyna Urbanek-Trzeciak, Product Manager (Data Engineering)
"I had the pleasure to work with Bartosz at Fandom, shaping the Data Engineering department. He has shown his engagement, diligence, and customer-centric approach in every activity. Bartosz was always willing to help customers and teammates with extreme patience and a result-oriented attitude."
Krzysztof Pniewski, Product Manager
"It was my pleasure to work with Bartosz. He is a dedicated and experienced Data Engineer who showed a range of skills and readiness to help. I appreciated that I could count on him to lead sophisticated technical projects. Highly recommend!"
Mariusz Kuriata, Senior Manager of Engineering - Head of Ops

One Call. One Week. Your Zero-Hallucination Roadmap.

Prevent public failures before they happen. Let’s make your AI trustworthy—today.

Request Your AI Risk Audit

Recent Articles on AI Implementation Reliability:

The AI Hallucination Crisis in Fintech

AI hallucinations are fintech's biggest risk. Discover why most deployments stall and what CTOs can do to secure their AI stack.

A Framework for Measuring and Fixing AI Hallucinations

AI hallucinations are killing trust in your product. This guide helps you measure, debug, and prevent them — starting today.

How I Transformed a Failing AI System into 99.7% Accuracy in 4 Days (And Eliminated a €20M Regulatory Risk)

Learn how to implement production-ready AI systems using a systematic approach focusing on error analysis rather than complex models. This case study shows how to transform unreliable AI prototypes into trusted production systems that eliminate regulatory risks and deliver real business value.

How to Make AI Evaluation Affordable: Research-Backed Methods to Cut LLM Evaluation Costs

Why are your AI evaluation costs spiraling out of control, and what are the proven methods to reduce them without sacrificing quality?

The Hidden Reason Your RAG System Is Failing - The Problems Caused by Approximate Nearest Neighbor Search in Vector Databases

Discover why your RAG system might be failing due to Approximate Nearest Neighbor search limitations in vector databases. Learn how compute budgets affect search accuracy, why metadata filters complicate retrieval, and implement practical solutions to dramatically improve your RAG performance.

Why is it so hard to correctly estimate AI projects?

Why can't you estimate an AI project correctly and can you do anything about it?

Building Reliable AI: A Testing-First Approach

Learn how to properly test AI systems using familiar software testing concepts. Discover key metrics, alignment checks, and robustness testing strategies for reliable AI deployment.

From API Wrappers to Reliable AI: Essential MLOps Practices for LLM Applications

API wrapper or production-ready AI? Learn how proper LLMOps separates prototypes from reliable applications

Troubleshooting AI Agents: Advanced Data-Driven Techniques of Improving AI Agent Performance

Expert strategies for improving AI agent performance through better data retrieval, query generation, automated decision-making process, and response generation. The article covers data collection, metrics, and techniques to improve the agent's performance.

Comprehensive Guide to AI Workflow Design Patterns with PydanticAI code examples

Learn how to implement AI workflows and autonomous agents with PydanticAI. This guide shows an example implementation of patterns described in the Anthropic article 'Building effective agents' such as prompt chaining, routing, parallelization, and orchestrator-workers.

How Much Data Do You Need to Improve RAG Performance?

A data-driven approach for improving RAG performance. Learn how to gather data and how much data you need for RAG, fine-tuning LLM, and training a specialized LLM from scratch.

The Ultimate 2025 Guide to Prompt Engineering

Discover the difference between proven prompt engineering techniques and tricks

Want a roadmap, not just reading? Book your audit here.

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