AutoETL
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AI-native data infrastructureEarly access

AI-Powered
Data Automation

AutoETL turns data engineering standards into executable policy. Connect sources, define the rules once, and generate governed warehouse layers in minutes.

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30 min
to a staging layer
10×
less repetitive ETL effort
Every run
schema-aware operations
Live topology / build 042 compiling
POSTGRES prod.orders ORACLE erp.customer SFTP /*.csv AUTOETL COMPILER STAGE normalized CORE historized MART ready to use schema +4 fields drift reconciled ✓
01A live model of the product — no stock “AI” imagery.
PostgreSQLSQL ServerOracleMySQLFlat filesFTP / SFTPPostgreSQLSQL ServerOracleMySQLFlat filesFTP / SFTP

01 / The system

ETL is not a drawing problem.
It’s a systems problem.

Most pipeline tools help engineers draw more pipelines. AutoETL encodes how your team engineers data — then applies that operating model everywhere.

Source structures become metadata. Engineering conventions become policy. Repetitive implementation becomes generated, governed infrastructure.

pipeline.autoetlpolicy / v1.8
source: crm.leads
mode: incremental
watermark: updated_at
history: scd_2
target:
  stage: stg_leads
  core: dim_lead
on_schema_change: reconcile
Build outputlive
00:00.4

Source schema detected

18 fields
00:03.1

Policy validated

passed
00:11.8

Stage layer generated

ready
00:18.2

SCD history applied

ready
00:18.4

Operational log opened

running

One small declaration. A repeatable operating model.

02 / Platform principles

Built like infrastructure.
Operated like software.

No chatbot theater. No magic wand. AI works inside a governed system with explicit rules, observable output, and reproducible behavior.

01

Declare policy once

Capture naming, typing, incremental loading, history, and delivery standards as reusable configuration.

02

Generate the layers

Build production-ready staging and warehouse structures without repeating the same implementation work.

03

Adapt to source change

Detect structural drift, regenerate the model, and keep delivery moving with a traceable decision path.

04

Observe every run

Make outcomes, failures, and changes visible through consistent operational and audit-ready logs.

30min

From connection to a generated staging layer.

03 / Speed with control

Fast is useful.
Repeatable is transformative.

AutoETL compresses the repetitive part of data engineering while keeping the operating model explicit. The goal is not to remove engineers. It is to give them leverage.

10×less ETL effort
0manual maps required

04 / Operating model

From source to trusted data.

Four deliberate moves replace a long chain of tickets, mappings, scripts, and fragile handoffs.

  1. 01

    Connect

    Point AutoETL at operational databases, files, or secure transfer endpoints.

    source.online
  2. 02

    Declare

    Choose reusable ingestion, versioning, and delivery policies.

    policy.valid
  3. 03

    Generate

    Build the required data layers and reconcile source changes.

    layers.ready
  4. 04

    Operate

    Run with traceable outcomes and one consistent model.

    system.live

05 / Connectivity

Meet the stack
where it is.

Start with the systems already running your business. Extend the connector layer as the operating model grows.

01PostgreSQLRelational
02SQL ServerRelational
03OracleEnterprise
04MySQLRelational
05Flat filesCSV / structured
06FTP / SFTPSecure transfer

Build the next data layer differently

The next pipeline should make
the one after it easier.

Bring us one source, one warehouse target, and one real engineering standard. We’ll show you what AutoETL can generate.

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