QA Engineer Helping Teams Use AI in Testing
20+ years in software QA and test automation. Now focused on helping QA teams adopt AI - with honest, practical guidance.
My Journey
I'm Oleg Neskoromnyi - a QA leader, test automation architect, and AI testing strategist with over two decades of hands-on experience in software quality.
My path wasn't typical. I started with engineering degrees from Ukraine - Mechanical Engineering from the Military Aviation Engineering Academy and Computer Science from Radio-Technical College. That military precision never left me. It shaped everything about how I approach testing: systematic, thorough, zero assumptions.
I've led QA teams, built enterprise automation frameworks from scratch, and worked across industries from healthcare to data security to automotive at General Motors. Along the way, I earned certifications in Scrum, Databricks, and Tricentis Tosca - not for the credentials, but because understanding the full stack makes you a better tester.
I also taught QA at TestPro - evening classes covering everything from testing fundamentals to automation, for career-changers breaking into the field. That experience taught me how to explain complex testing concepts so they actually stick. Nothing makes me prouder than running into former students at conferences, meetups, and social media chats - and seeing them doing great in the field.
Then AI changed everything.
I didn't panic. I experimented. I built custom GPTs, created AI-powered testing agents, and developed entire automation workflows where AI handles the repetitive work while humans focus on what matters - critical thinking, edge cases, and real quality.
That's the core belief behind QaHub.AI: AI isn't here to replace testers. It's here to multiply their impact.
Where It All Started: Sarah
It started with a bigger question: what if AI could handle not just test cases, but the entire QA process - from discovering requirements to test planning to execution and analysis?
Sarah - Smart Test Management Framework - was my first AI-powered QA tool. The idea was simple: feed Sarah your API documentation, requirements, or even UI screenshots, and get back comprehensive test plans covering happy paths, edge cases, and error conditions - instantly. No more weeks of manual test creation. Sarah could generate test scenarios in a few minutes and suggest automation approaches for Cypress, Playwright, REST Assured, or Postman - with a human always reviewing and refining the output.
Sarah is still available as a custom GPT, though I've stopped actively developing her. Here's why.
She taught me something bigger than test generation. She showed me that one AI tool trying to do everything is the wrong approach. The real power comes from specialization.
That insight became the architecture behind everything I build today: an army of small agents, where each agent does one thing - but does it exceptionally well. One agent analyzes your codebase and extracts every business rule. Another takes those rules and creates a test plan with full coverage. A third writes the automation code. Another runs the tests and analyzes failures. Yet another validates that nothing was missed.
Each agent validates its own work. Each agent follows one absolute rule: no assumptions - if the information isn't explicitly in the requirements, the agent stops and asks. Together, they form a complete AI-powered QA pipeline that's more thorough than any single tool could ever be.
Sarah was the spark. The agent army is the fire.
What Drives Me
Most QA professionals I talk to feel one of two things about AI: fear or hype. I've been through both. What I landed on is something more useful - practical clarity. I know what AI can actually do for testing today because I use it every single day. I build agents that analyze APIs, generate test plans, write production-ready automation code, and catch bugs that manual review would miss. The question that drives me isn't “will AI replace testers?” - it's “how fast can I make a tester 10x more effective with AI?”
My Approach
Experiment. Fail. Learn. Use. That's my cycle, and I don't pretend otherwise. I'm not an “AI thought leader” writing theory from the sidelines. I'm a builder. When I write about a technique on this blog, it's because I've already used it on a real project, hit the walls, found the workarounds, and have the battle scars to prove it. Every framework, every agent, every workflow I share here - I built it first and used it myself.
My Philosophy
Build, Don't Just Read
I'm a hands-on learner who'd rather build a broken prototype than read a 200-page manual. Every tool, framework, and AI agent I teach about on QaHub.AI started as my own experiment. If it didn't test it, it doesn't make it to the blog.
No Hype, No Fear
AI in testing isn't magic, and it's not a threat. I give you the real picture - what works, what doesn't, and when AI is the wrong tool for the job. You'll get honest assessments backed by actual implementations, not marketing slides.
Multiply, Don't Replace
The best QA professionals I know aren't being replaced by AI - they're becoming unstoppable with it. My mission is to show you exactly how to get there, with practical skills you can apply this week, not someday.
Let's Connect
Want to discuss AI in testing, share feedback, or just say hi? I'm always happy to connect with fellow QA professionals.