Thinking at the Frontier of Regulated AI
Perspectives on agentic AI, compliance architecture, and what it takes to build AI systems that regulators trust.
Turning Forging Video Into Burn-Off Timing Data
Used a lightweight vision-language model to pinpoint the exact moment burn-off and vaporization begin in metal forging videos, converting archive footage into a measurable process signal.
Operational Cookbooks from Tribal Knowledge
A major propulsion OEM transformed decades of fragmented maintenance and operator knowledge into a living cookbook system that gets smarter with every repair cycle.
Vector Databases vs. Graph RAG for Agent Memory: When to Use Which
Everyone reaches for a vector database by default. Most agents do not actually need one; they need to remember how things connect. Here is how I decide between vector search and graph RAG, and when I just use both.
From Autocomplete to Assistant: How LLMs Are Trained (and Why They Still Get Things Wrong)
A raw transformer is a brilliant parrot, not an assistant. Turning one into something helpful takes three distinct stages, and understanding them tells you exactly why these models still confidently make things up.
The Transformer, Explained Without the Math
The transformer is the architecture behind every modern AI you've heard of. You can understand how it works without a single equation. It comes down to three moves: chop, place, and look.
What's Actually Inside an LLM? How AI Learned to Read
Everyone talks about large language models like they understand language. They don't, not the way you do. What they actually do is stranger, simpler, and worth understanding before you trust one with anything.
Why Agentic AI Is Different in Regulated Industries
General-purpose AI agents assume a permissive operating environment. Regulated industries do not have that luxury. Here's what changes when compliance is non-negotiable.
Three Patterns for Multi-Agent Compliance Workflows
Not all agentic architectures are created equal for compliance use cases. We walk through three production-proven patterns and when to use each.
Building and Testing Your First Model
A step-by-step walkthrough of training a prediction model, evaluating whether it works, and interpreting the results, explained in plain English with no math required.
Choosing Your First AI Project
How to pick a problem that AI can actually solve, frame it as a prediction the system can learn from, and set yourself up for a first project that delivers visible value.
Is Your Data Actually Ready? A Practical Audit
A hands-on guide to auditing your operations data, finding the gaps that matter most, and deciding which AI project to tackle first based on what your data can actually support.
What "Learning from Data" Actually Means
A practical explanation of how AI systems learn from historical data, what that process looks like in an operations context, and why your existing data is the raw material for better predictions.