Nexlytics vs Power BI Documenter for reviewing a model

Describe vs judge: a genuinely different deliverable

A documentation tool tells you what is in the model. It catalogs measures, maps table relationships, notes where a field is used across reports. That output has real value when you are handing a model to a new developer, writing internal reference docs, or onboarding a support team.

Nexlytics is built for a different question: is this model healthy, and where are the risks? It runs five AI agents across the file, scores each dimension, and surfaces the specific issues that are most likely to cause wrong numbers, slow refreshes, or governance failures. Description and judgment are related but not interchangeable.

The model looks fine on paper but the numbers are wrong

Documentation tools faithfully record what they see. If a measure has a logic error, they document the logic error. If two measures look identical but produce different results under certain filter contexts, documentation records both measures without flagging the divergence.

Nexlytics checks for the things that are not visible from structure alone: unused tables adding file weight, RLS rules that do not cover every role, measures that diverge from their expected counterparts, and dependency chains that silently fail on refresh. It finds what is wrong, not just what is there.

Where documentation tools genuinely win

If you need to produce a comprehensive written record of a model for a handover pack, a contract handover, or a reference library, a dedicated documenter produces more thorough output than Nexlytics. It will enumerate every field, every dependency, every description field. For that specific deliverable, the right tool is the right tool.

Nexlytics produces a documentation summary as part of the audit. But if the primary goal is reference documentation rather than health assessment, a documentation tool gives you more depth on that dimension.

Using both

Many teams run Nexlytics first to find out whether the model is worth documenting in its current state. If the health score flags critical issues, they fix those before investing time in a handover pack. Once the model passes audit, documentation tools have something accurate to catalog.