Insights
Field notes, essays, and analysis exploring AI-native systems, logistics intelligence, and modern enterprise architecture.
Why RAG Fails in Production — and What to Do About It
Retrieval-augmented generation works remarkably well in demos. Operational environments are a different problem entirely. Real enterprise data is messy by nature.
Fine-tuning vs. Prompting — The Real Tradeoff
The debate between fine-tuning and prompt engineering isn't just technical — it's an operational decision. Here is a guide on where the trade-off actually lies.
Text-to-SQL for Operational Analytics — Beyond the Toy Examples
Making natural language querying work against real freight and procurement data requires hybrid search, metadata filters, self-correction loops, and context budgeting.
LLMOps — What Enterprise Teams Miss When Moving to Production
Deploying a prototype is straightforward. Operating one in production requires observability, prompt versioning, structured evaluation frameworks, and context window discipline.
From Dashboards to Intelligence Systems
Why visualizing data is no longer enough — and what comes after the dashboard era.
Building AI Procurement Intelligence Systems
Procurement workflows are fragmented by design. RFQs arrive as spreadsheets, PDFs, emails, pricing tables, carrier notes, and operational updates — usually spread across disconnected systems.
