Needed a Product Engineering Team, So I Built One
Why I built the platform that guides your coding agent with production-ready constraints
The Problem I Couldn't Ignore
I was building TextGuardian, a cybersecurity app to protect against scams. With AI coding agents, I could build features incredibly fast. But fast doesn't mean production-ready.
My coding agent would generate authentication systems without proper rate limiting. Build features without considering scalability. Create APIs without thinking about error handling. The code worked—but it wasn't secure, scalable, or reliable.
I needed something that would inject security, scalability, and reliability constraints into my agent's context. Not after the code was written, but before. I needed to guide my coding agent the same way a senior engineering team would guide implementation.
That's when I realized: we have AI for coding, but not for engineering discipline.
So I built Cutline. Not as another coding agent, but as a product engineering platform—a system that extracts technical constraints, validates them through pre-mortem analysis, and injects production-ready guidance directly into your coding agent's context.
Background
Cornell University - Graduate studies in Cognitive Science and Machine Learning. Focused on how humans make decisions under uncertainty and how AI can augment (not replace) human judgment.
Consultant, Wells Fargo - Built anti-fraud machine learning systems. Learned that the hardest part isn't the ML—it's understanding what problem you're actually solving and whether the solution will work in production.
- eBay - Built ML systems to detect fraud, abuse, and policy violations at massive scale
- LivePerson - Developed AI to protect customer conversations and prevent bad actors
- Upwork - Created systems to ensure platform safety and trust for millions of freelancers
Why My Background Matters for Cutline
Building AI for Trust and Safety taught me something crucial: AI systems need constraints to prevent failure, not just features to enable success.
When you're protecting millions of users, you can't wait for security issues to emerge in production. You need to anticipate risks, define constraints upfront, and validate your approach before deploying. That's exactly what Cutline does for vibecoding—it shifts security, scalability, and reliability left.
Cognitive science gave me the frameworks for understanding how people think about risk and constraints. Machine learning gave me the tools to automate constraint extraction and validation at scale.
Leading AI teams at scale taught me that production-ready isn't just about writing code—it's about writing code that's secure, scalable, and reliable from day one. That's what Cutline helps coding agents do.
Product Philosophy
I believe the future of software development isn't about building faster—it's about building with the right constraints from the start. AI coding agents have made the "writing code" part ridiculously fast. But nobody's solved the "production-ready constraints" part.
Cutline isn't trying to replace developers or engineering teams. It's trying to make coding agents better. To compress months of architectural review into minutes. To surface the security, scalability, and reliability issues before code is written.
The best developers don't just ship fast. They ship production-ready code fast.
That's what Cutline enables. Not another coding agent, but a product engineering platform that guides your coding agent with validated technical constraints.
What We're Building
Cutline is in active development, with a focus on three core capabilities:
Cutline also provides product validation capabilities—helping you validate ideas, test with AI personas, and build product intuition over time. Because the right thing built the right way still needs to be the right thing.
Want to Chat?
I'm always happy to talk about safe vibecoding, production-ready AI code, or building with engineering rigor in the AI era.