AutoETL

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.

See the system
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

06 / Enterprise trust

Trust is not a badge.
It is a system.

AutoETL is being built for teams that need speed without surrendering control. The technical preview makes system boundaries, operating evidence, and deployment responsibilities explicit.

01Security

Security by design

Credentials stay inside controlled runtime boundaries. Connections are scoped for least-privilege access and encrypted transport before a production path is approved.

  • secrets / server-side
  • transport / encrypted
02Boundaries

Customer boundaries

Configuration, execution context, and data paths are separated by customer. Generated assets remain attributable to the policy and version that produced them.

  • tenant / isolated
  • policy / versioned
03Evidence

Operational evidence

Runs expose inputs, policy version, changes, outcomes, and failures so engineering teams can investigate and reviewers can follow the decision path.

  • runs / traceable
  • changes / logged
04Deployment

Scoped deployment

Start with one bounded source-to-target case. Network, identity, retention, and ownership are reviewed with the customer before expansion.

  • scope / bounded
  • ownership / explicit

Company & accountability

A direct technical relationship, from first review to production.

AutoETL is an early-stage product shaped by hands-on data engineering experience and developed through controlled technical previews. Customers work directly with the team building the platform.

Status
Private technical preview
Engagement
Architecture review + scoped validation

Compliance roadmap

Evidence before claims.

AutoETL does not claim certifications it has not earned. Enterprise readiness advances through documented controls, reviewable evidence, and independent validation.

  1. 01
    Foundation — now

    Documented data flows, scoped access, customer boundaries, and traceable execution.

  2. 02
    Operational readiness

    Security documentation, data-processing terms, access-control model, and incident procedures.

  3. 03
    Independent validation

    Testing and certifications are announced only after successful completion.

Build the next data layer differently

The next pipeline should makethe one after it easier.

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

Private technical preview

Bring us a real pipeline.
We’ll show you the system.

Tell us where your data engineering team is today. We’ll reply personally with the best next step.

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