ACBDiDATAP.AI

Data Governance
Evidence by Construction

Where did this number come from? Who can see it? Is the table open or locked in? Our layer answers all three for every column, on every build — from the dbt project you already have, on Iceberg storage you own, inside your VPC in Australia.

Column-level lineageData quality testsPII map & maskingIceberg you ownRuns in your VPC
See the live lineage view →Talk to us →

Four things every regulated data team gets asked

Lineage · Quality · Personal data · Ownership — one layer, generated on every build.

1

Column-level lineage

Click any number, see every source it was computed from.

  • •Table and column lineage from the dbt project you already run
  • •No migration, no new catalog, no per-seat licence
  • •Lives inside your dbt docs: click a table → its documentation; every docs page links back
  • •Stored per build in your own database, queryable in plain SQL
2

Data quality, generated

Tests are written with the pipeline, not after it.

  • •Row-count reconciliation between layers
  • •Range, set and null expectations derived from the data profile
  • •Failures repair themselves before the build goes green
  • •Results kept per run for audit
3

Personal data, followed

A scanner finds email. Lineage finds the six columns derived from it.

  • •Classification propagates through every rename, join and hash
  • •Masking policy per sensitivity level, ready to apply — hash, redact, partial
  • •AI answers respect the same classification
  • •Default: classify, don’t block — restrictions only when you decide
4

Open storage you own

Every table the layer builds is Apache Iceberg on your object storage.

  • •Snowflake, Databricks, Athena, ClickHouse read the same files
  • •Move engines without moving data
  • •Point-in-time snapshots for regulators
  • •Nothing leaves your VPC, in Australia

Proof, not promise

Measured on DATAP.AI’s own production pipelines, September 2026. Your numbers on your project in one afternoon.

90 models · 903 edges
column lineage traced on a real dbt project, zero manual mapping
49 columns
personal data found by propagation — 22 never named email or phone
31 / 31
models and tests green after automatic repair
1 click
from a number to its four raw source columns across three tables

How it works

1

Point it at your dbt project

Read-only. Nothing changes in your warehouse.

2

Every build produces the evidence

Lineage, classification, tests — written into dbt docs and your own tables.

3

Decide what to enforce

Masking, access, human approval — policy in a config table, changed without code.

One product with your dbt docs

Same project, same URL, two views. Nothing to install beside dbt docs.

Lineage view with the provenance of close_usd highlighted
Lineage view. Click a column, its provenance lights up across every layer.
dbt docs Code tab showing the table is configured as Iceberg
dbt docs. Click the table, see the logic — and that it is Iceberg on your storage.
Open the live lineage view →|Open the live dbt docs →

Made for Australian regulated finance

Runs in your AWS account in Australia. Models you choose. Formats you own. Designed for IRAP-aligned controls.

ObligationWhat you get
APRA CPS 230 / CPS 234Per-column classification, source-to-report lineage, reproducible per build
ASIC reportingLineage from source systems to each reported field
AUSTRAC record-keepingClassification and retention rules, snapshot evidence
Privacy Act APPsPersonal-information inventory, who can see it, masking evidence

See it on your own dbt project

One afternoon, read-only, inside your account. Lineage and a personal-data map you did not have yesterday.

Book a 30-minute walkthroughHow AI Governance fits →