Perspectives, methodology notes, and field observations from the Deaimer team. We write to sharpen our own thinking — and because the AI data industry is better when practitioners talk openly.
The gold-set-only approach to rater quality is an incomplete picture. Here's what a disciplined calibration cadence actually looks like.
Lessons from running hundreds of parallel annotation workflows — and why the simplest scheduling rule almost always wins.
How we monitor rater agreement over time, what we do when a senior rater starts drifting, and the dashboards that flag it in real time.
What our MSA refusal language actually covers, how we make judgment calls, and why this isn't just corporate positioning.
Three patterns we're seeing across our enterprise and frontier-lab engagements heading into the year.
Rubric design is one of the highest-leverage activities in data operations. Here's what separates great rubrics from frustrating ones.
An honest inventory of the mistakes we made, the lessons we took, and what we're doing differently going into 2026.
Public benchmarks are saturating. That doesn't mean evaluation is solved — it means evaluation work is just beginning.
Transparency is a process, not a PR exercise. What we found in our own operation, what we're changing, and why we shared it publicly.
The AI data industry is consolidating — but not in the direction most analysts expect. Here's what we're seeing on the ground.
Agentic evaluation is different — multi-step, tool-using, stateful. What we've learned from designing evals for these systems.
The operational playbook for ramping up a specialized annotator team — whether it's radiologists, attorneys, or CFAs.
Tell us what you're training, aligning, or evaluating. We'll map a delivery plan, staffing model, and timeline within one working week.