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

Your AI Is Lying to Customers
Stop AI hallucinations before they cost you customers, compliance, and credibility.
Prevent AI Hallucinations in Production
Get a personalized risk audit + 7-day roadmap to eliminate hallucinations from your customer-facing AI systems.

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.

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 AuditRecent 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
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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
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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