field notes.
Observations from the front lines of AI implementation. No theory, just practice.
Why Most AI Pilots Stall Before They Ship
Most companies run AI pilots the same way they run software projects. That is the first mistake. The gap between a working demo and a production system is where most initiatives quietly die.
Building Internal Tools with LLMs: A Practical Guide
The best internal tools are the ones nobody has to think about. Here is how to build LLM-powered tools that your team will actually use, not just admire in a demo.
The Real Cost of Build vs. Buy in AI
Every AI vendor will tell you to buy. Every engineering team will tell you to build. The right answer depends on something neither side talks about: your rate of change.
Automating Deal Flow Analysis with AI Pipelines
We built a system that reads incoming deal memos, extracts key metrics, scores them against firm-specific criteria, and surfaces the top candidates. Here is how it works.
Why Forward-Deployed Engineering Works
The best software is built by people who understand the problem firsthand. Forward-deployed engineering is not a staffing model. It is a fundamentally different approach to building.
Data Pipelines That Do Not Break at 3 AM
We rebuilt a data pipeline that had been waking up the on-call engineer three times a week. The fix was not better monitoring. It was better architecture.