Blog
Notes from the field.
Practical writing on what actually works when you put AI into production — no hype, no vendor pitches.
Why most AI pilots never make it to production
The gap usually isn't the model — it's what happens between "it works in a demo" and "the team relies on it every day."
Building an evaluation set your team will actually trust
A practical framework for scoring AI output against real cases, before you ever ship to production.
How to pick the first workflow to automate
Not every process is a good candidate. Here's the checklist we use with clients before scoping any build.
Retrieval vs. fine-tuning: a decision guide
Most teams reach for fine-tuning first. In our experience, it's usually the wrong first move — here's why.
Monitoring AI systems the same way you'd monitor infra
Latency and uptime aren't enough. What we track once an AI system goes live, and why.
Getting your team to actually adopt a new AI tool
Adoption is a change-management problem more than a technology problem. What's worked across our engagements.