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Holistics Docs

The AI analytics platform built on a programmable semantic layer and analytics-as-code, so AI and humans reason from the same governed business logic.

What is Holistics?​

Holistics is an AI analytics platform for teams that need governed, trustworthy answers across dashboards, self-service, and AI.

With Holistics, you can:

  • Trust your AI analytics. Holistics AI reasons over your governed metric definitions rather than raw tables, so answers stay consistent with the numbers your team already uses.
  • Give business users real self-service. Business users can answer their own questions inside the governed semantic layer instead of filing analyst tickets. The layer is expressive enough to handle complex follow-ups (cohort retention, period comparisons, ratios across grains), so exploration doesn't dead-end in spreadsheets.
  • Manage analytics like software. Every model, metric, and dashboard is defined as code, version-controlled in Git, and promoted through environments. Pull requests, code review, and rollbacks all work the way they do for application code.

How Holistics is built differently​

Most BI tools now ground their AI in a semantic layer, but the layer underneath can only express first-order queries (slice, filter, group). AI hits a ceiling on real analytical questions like period comparisons, cohort retention, and ratios across grains, and self-service stops at simple breakdowns. Holistics is built on a deeper foundation:

  • A programmable semantic layer. Most BI tools define their semantic layer in YAML configs, which are schemaless, error-prone, and require Jinja workarounds for any reuse. Holistics uses AML, a typed modeling language where models, dimensions, measures, and relationships are first-class language constructs. Modules, extends, partials, conditionals, IDE tooling. A real language built for analytics modeling, not generic key-value configs.
  • A composable query language on top. Most semantic layers treat metrics as SQL strings that can't combine or reuse. AQL treats metrics as first-class composable objects, so period comparisons, cohort retention, and ratios across grains stay inside the metric layer instead of leaking into derived tables and spreadsheets.
  • Analytics-as-code infrastructure. Most BI tools are UI-first with Git bolted on as an afterthought (if at all). In Holistics, every definition is version-controlled in Git from day one, reviewable through pull requests, and promotable through environments. Code is the source of truth.
How Holistics fits in your Data Stack

Where to start​

New to Holistics? Start by understanding the product, then try it hands-on.

Understand Holistics​

Why Holistics

What problem we solve, and how we solve it differently.

How Holistics works

The architecture and the end-to-end workflow.

Key concepts

The terminology and core abstractions you'll see throughout.

Product philosophy

The principles behind our product decisions.

Try it hands-on​

Walkthrough tutorial

Build a dataset and dashboard step by step.

Sample demo environment

A live, sample workspace you can poke around in.

Playground

Write AQL and AML right in your browser.

Evaluating something specific?​

Holistics AI

AI on top of the governed semantic layer.

Open Semantic Layer

Query your governed metrics programmatically.

Embedded Analytics

Put dashboards and AI directly in your product.

Coming from Looker

Map LookML concepts onto Holistics.

Stay in the loop​

Release notes

What shipped recently.

Roadmap

What's coming next.

Community forum

Questions, feature requests, tips and tricks.

Older versions of Holistics​

This site covers Holistics 4.0. If you're on an earlier version:

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