Production AI, explained plainly.
Writing from the team building AI systems every day — on what works, what doesn't, and what makes the difference between a demo and a deployed system.
Document Intelligence Cost Optimization: How to Build an Extraction Pipeline That Doesn't Cost You the Savings You're Chasing
Most document intelligence cost problems aren't LLM pricing problems. They're pipeline design problems. Here's how to instrument, tier, and govern extraction costs without trading accuracy for spend.
Document Intelligence for Multi-Tenant SaaS: What It Takes to Build Tenant-Isolated Pipelines in Production
Building document intelligence for multiple customers isn't just a single pipeline with tenant filtering added. It requires isolated extraction schemas, per-tenant validation rules, configurable exception routing, and audit trails.
Building Reliable AI Agents: Why Guardrails Matter More Than Model Choice
The difference between a demo AI agent and a production one isn't the model — it's the guardrails, fallback logic, and observability wrapped around it.
Why Most AI Proofs of Concept Never Reach Production
The gap between a working prototype and a production system is wider than most teams expect. Here's what's missing and how to bridge it.