Every enterprise data platform has a layer for the plumbing and a layer for the definitions. Almost none have a layer for confidence.
The truth layer is a machine-readable confidence layer that sits between your data platform and your decisions, scoring whether a metric can be trusted right now, by humans and AI agents alike.
Built and written by Justin Nixon. Twenty years building governance infrastructure that makes enterprise data trustworthy enough to act on.
Published so far
One launch a week. Each is an essay for the deep read and an interactive page for the hands-on version.
A count an AI agent acted on turns out wrong. File the correction and watch the gated list fill with every action that used the old value, then clear each one. Two take an alert. The slide can’t report use, so it won’t take an assumption.
One trust score read by two different sets of constants, the thresholds a deployment published and the bands the grading path actually used. Drag a score into the gap and watch an agent that obeyed its contract get recorded as wrong.
Twenty-six weeks of daily sessions with an AI that could not see the work it was helping with. I expected half the rules to vanish when I fixed that. I counted. The rules stayed. The machinery went.
Follow one revenue figure through a fourteen hour delay and a boardroom disaster, then replay the same hours with a truth layer catching it at every stage. Told at three altitudes. Start here if you start anywhere.
An interactive walkthrough of the machine-readable confidence signals that travel with every metric.
Most data platforms have two layers. Almost none have the third. See where the gap is.
“Trust is built by humans, consumed by machines.”
Justin Nixon, from the commute notes