Lessons learned from building AI products across healthcare, government, financial services, and more.
After shipping 18+ AI products, we've identified the patterns that separate successful deployments from expensive experiments. The difference rarely comes down to the technology itself.
Starting with technology instead of the business problem
Underestimating the importance of data quality
Building trust through iterative delivery
The hype around multi-agent architectures is real, but so are the failure modes. Here's how to decide if your use case actually needs agents working together.
Retrieval-Augmented Generation is table stakes. Here's what separates enterprise-grade knowledge systems from demos that break in production.
Most AI governance frameworks are either too restrictive to ship anything or too loose to prevent problems. Here's how to find the balance.
HIPAA, FDA, and clinical validation requirements don't have to slow you down. Lessons from deploying AI in regulated healthcare environments.
Product management for AI is fundamentally different. Here's the framework we use to scope, prioritize, and ship AI features that users actually adopt.
Public benchmarks don't tell the whole story. Here's how to evaluate LLMs for your specific enterprise use case.
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