About the Guest
Anton Tiscovschi is the Founding Engineer at Kreoh, an AI engine built for R&D tax consultants. He shares his journey from computer science and automation engineering into building an applied AI startup focused on real-world business problems.
AI Hacks Podcast with Anton Tiscovschi - Kreoh Founder
Building an AI Engine for R&D Tax Consultants
Episode Description
In this insightful conversation, Liana sits down with Anton Tiscovschi, Founding Engineer at Kreoh, to discuss building an AI engine for R&D tax consultants. This episode explores the challenges of building an applied AI startup focused on real-world business problems.
Anton shares his journey from computer science and automation engineering into building an applied AI startup focused on real-world business problems. He discusses the unique challenges of building an AI engine for R&D tax consultants and how Kreoh is solving real-world business problems with AI.
Key Topics Covered
- •From Computer Science to AI Founder: Anton's journey from automation engineering to building Kreoh
- •Building AI for R&D Tax Consultants: Solving real-world business problems with applied AI
- •The Reality of AI Product Development: Why building AI products is harder than it looks
- •Human Side of AI Engineering: The people challenges behind building AI systems
- •AI in Modern Startups: How AI is changing the way engineers learn and solve problems
- •From Theory to Production: The challenges of turning AI concepts into working products
- •Client Problem Solving with AI: How Kreoh addresses real customer needs
- •Future of Applied AI: Where AI engineering is headed in business applications
Key Insights
- 💡Building AI products is harder than it appearsThe gap between AI theory and real-world application is much larger than most people realize
- 💡Applied AI solves specific business problemsSuccess comes from focusing on real customer needs rather than technology for its own sake
- 💡Human challenges are bigger than technical challengesThe people and process aspects of building AI systems often outweigh the technical difficulties
- 💡AI is changing how engineers learn and solve problemsModern startups need to adapt their engineering approaches to leverage AI effectively
- 💡From theory to production requires practical experienceAcademic knowledge needs to be complemented with real-world implementation challenges
Who Should Listen
This episode is essential for aspiring AI founders and engineers, computer science students interested in AI entrepreneurship, startup builders exploring AI applications, engineering leaders considering AI solutions, and anyone curious about the reality of building AI products. If you're thinking about starting an AI company or want to understand what it really takes to turn AI from theory into production, this conversation provides critical insights you need to know.
Action Items for AI Founders
- →Focus on real business problems rather than technology for its own sake
- →Understand that building AI products takes longer and costs more than expected
- →Prepare for human and process challenges, not just technical ones
- →Get practical experience beyond academic knowledge
- →Build a team that understands both AI and the business domain
- →Be prepared to adapt your approach based on real-world feedback
- →Start with specific, solvable problems before expanding scope
Links & Resources
- Kreoh.com - AI engine built for R&D tax consultants
- Anton Tiscovschi on LinkedIn - Connect with Anton and follow his journey
- SonnyLabs.ai - AI security platform protecting applications from prompt injection attacks
- SonnyLabs AI Security Scanner - Detect prompt injection attacks and AI vulnerabilities in real-time
- Interactive Demo - See how prompt injection attacks work and how to prevent them
🎙️ Special Thanks
Special thanks to Anton Tiscovschi for sharing his journey and insights into building applied AI startups. This episode provides valuable lessons for anyone interested in the reality of AI product development and entrepreneurship.