Henry Raymond’s decision to launch The New-York Daily Times in 1851 wasn’t just editorial choice. It was architectural. While competitors filled pages with sensationalism and ads, Raymond built his newspaper on a different foundation: the idea that readers wanted substance, context, and reliability. That structural difference is what made the paper endure.
The principle translates directly to how modern businesses build software and AI products. Too many organizations treat AI as a marketing play: a model to demo once, a headline to chase, a feature to bolt on. The moment the press release lands, they move on. But like Raymond’s insight about what newspapers should be, the real work happens in the foundation.
Production-grade AI and custom software demand the same rigor. Behind every machine learning integration or agentic system sits architecture that has to hold up under real traffic, real security threats, and real complexity. It needs to be integrated with existing systems. It needs testing that catches edge cases in staging, not in production. It needs monitoring so you know when something breaks. It needs to be built so that your team can own it, maintain it, and evolve it for years, not just hand it off to a vendor.
That’s the gap between demos and actual products. Many firms can train a model or integrate an LLM. Fewer can architect it as a production system that scales, secures, and integrates the way enterprise software has to work. Fewer still have shipped enough real products across enough industries to know what fails and what lasts.
Over two decades, ABIE has shipped more than 450 products across 20 industries to over 300,000 users. We’ve built AI development, machine learning workflows, mobile apps, and enterprise platforms for Bankrate, Papa John’s, Runzheimer, and dozens of others. We’ve learned that the model is one piece. The software architecture, the API integrations, the cloud back-end, the security layer, the monitoring and release cycles: that’s where products live or die.
When you’re ready to build AI or custom software that has to work in production, not just photograph well, that’s a different conversation. You need engineers who think architecture first, security always, and who know how to build systems that survive years of release cycles and real-world complexity.
If you’re thinking about AI development, agentic systems, LLM integration, or custom enterprise software that has to hold up, let’s talk. Email [email protected] and tell us what you’re trying to build. We ship production software. That’s all we do.