What does Clarity actually do?

It connects to your existing database, lets your team define metrics once in a shared semantic layer, turns plain-English questions into SQL you can read and edit, and publishes the results as live dashboards and scheduled reports.

Does Clarity copy my data?

No. Clarity connects to your databases and warehouses and reads your schema — your source of truth stays untouched. Uploaded CSVs are the one exception and live in Clarity's managed storage.

Which data sources are supported?

PostgreSQL, MySQL, SQL Server, Oracle, ClickHouse, BigQuery, Snowflake, Redshift, plus CSV and Excel uploads.

How do I know an answer is trustworthy?

Every AI answer shows the SQL that produced it and the governed definitions it used. Metrics can be certified by an admin — the stamp drops automatically if the SQL changes — and every publish, edit, certification, and delete is recorded in a browsable audit trail.

How does the AI assistant handle my data?

Clarity uses a metadata-first approach: it sends schema and semantic context needed to generate governed SQL, not your raw rows. Your data is never used to train public models.

Do I need a data engineer to set it up?

No. Connecting a source takes a read-only credential and a few minutes. Modelling metrics is a form, not a codebase — though if you'd rather build transformations visually, the Flow Builder is there.

Is there a free plan?

Yes — the Free Trial includes 50 one-time credits, one database connection plus CSV uploads, and up to 1 million rows of storage. No credit card required.

Clarity

Analytics without black boxes

Stop being the SQL help desk.

Your team asks in plain English. Clarity writes the SQL and shows it to you before it runs — against metric definitions you certified. You approve the query, not answer the ticket.

Get started free See how it works

Sample data loaded — nothing to connect
No credit card
Your data stays in your warehouse

app.clarityanalytic.com

Clarity analytics workspace — a plain-English question, the SQL it generated, and the result table across regions and products.

Works with the stack you already have

PostgreSQL
MySQL
SQL Server
Oracle
ClickHouse
BigQuery
Snowflake
Redshift
CSV
Excel

Get started free

How it works

From your database to a decision, in four steps.

No pipeline to build. No warehouse to migrate. Clarity sits on top of what you already run and gets out of the way.

1
Connect
Point Clarity at the database you already run — Postgres, Snowflake, BigQuery, Redshift and more. It reads your schema in seconds. Your rows never move.
2
Model
Define each metric once — revenue, churn, active user — in a shared virtual schema. Everything downstream inherits that one definition.
3
Ask
Type a question in plain English. Clarity writes the SQL, shows it to you, and runs it only when you're happy with it.
4
Publish
Turn any answer into a live dashboard or a scheduled report. Viewers are unlimited on every paid plan.

Governance

Trust is a feature. It ships in the product.

Anyone can bolt a chat box onto a database. The hard part is an answer you can defend in an audit — where the definition is approved, the query is visible, and the change history has names on it.

Certified definitions

An admin stamps a metric as the trusted one. Edit the SQL and the stamp drops until it's re-approved — a changed definition never silently keeps its badge.

The SQL is always there

Ask in plain English, then read the exact query that answered — one click away on every AI answer, never hidden inside a chat bubble.

A browsable audit trail

Publishes, edits, certifications, deletions, membership changes — every sensitive action is recorded with who, what, and when, filterable from settings.

Lineage you can act on

Every metric shows the datasets it reads and the dashboards it feeds — so you know exactly what moves before you change anything.

Why Clarity

Most analytics stacks break in the same three places.

Numbers that don't agree

Finance reports 4.2M, sales reports 3.9M. Same warehouse, two people's SQL, and a meeting spent arguing about which is right.

One certified metric definition every dashboard, report, and AI answer reads from.

A queue for every question

Every “quick question” becomes a ticket for the one analyst who knows the schema — and the answer lands three days after it mattered.

Anyone on the team asks in plain English and gets a governed answer in seconds.

AI you can't audit

A chat box hands you a confident number with no query, no lineage, and no way to check it before it reaches the board deck.

Every query is visible and editable before it runs. Nothing executes behind your back.

Who it's for

Built for the whole team, not just the data team.

Pick the seat you sit in and see what your first week looks like.

Stop being the SQL help desk

You keep the definitions honest; everyone else self-serves against them. The schema you curate becomes the contract the whole company queries through.

See the semantic layer

Author virtual schemas and metrics once, with versioning, diff, and restore
Review the AI's SQL before it runs — edit it, save it, or reject it
Certify the approved definition — the stamp drops automatically when the SQL changes

Week one: Connect your warehouse, model your three most-argued-about metrics, and point the rest of the team at them.

Connections

Connect everything. Copy nothing.

Link Postgres, Snowflake, BigQuery, Redshift, MySQL, SQL Server, Oracle, ClickHouse — or upload CSVs and Excel. Clarity reads your schema; your data never leaves your warehouse.

Clarity Connections — choose a SQL database, Snowflake, BigQuery, Redshift, or Teradata.

Semantic layer

One language for every metric.

Define a virtual schema once — fields, types, business logic, shared metric definitions. Every dashboard, report, and AI answer uses the same source of truth. Versioned. Reusable. Trusted.

Virtual fields with formula + type
Schema versioning with diff and restore
Reusable across datasets, flows, and dashboards

Clarity Virtual Schemas — fields, types, sources, versioned.

AI assistant + SQL workbench

Ask anything. See the SQL.

Plain-English questions become governed SQL that respects your schema and access controls. Every query is visible — and editable — before it runs. Results render instantly with charts, filters, and group-bys.

Clarity Analytics — SQL results with month, region, product, units, revenue.

Labs

Documents in. Datasets out.

Define a template, drop in PDFs, text files, or audio. Clarity extracts typed fields with confidence scores, lets you validate and version, and publishes the result as a dataset the rest of the platform can query.

Multi-modal — PDFs, plain text, and audio transcription
Per-field confidence + governance review
Publish as a first-class dataset

Datasets & baskets

Every result, reusable.

Every connected table, uploaded CSV, Labs publish, and saved query becomes a first-class dataset. Save any SQL result as a refreshable basket, then feed it into flows, charts, and AI prompts.