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The warehouse bill grows faster than the data. And last quarter's number won't reproduce.

Laptop-sized jobs. Laptop-sized bills.

Run laptop-sized workloads on hardware you own — and get the same number on every run.

Become a design partnerFind your role

What changes for you.

Warehouse speed on a notebook

Revenue by town over 100 M property sales in 3.5 ms (warm, measured).

Smaller bills

Development, tests and mid-size jobs move from the per-query bill to hardware you own.

The same number every time

Reruns reproduce last quarter's figures to the digit.

Streams built in

Event-time windows, late data, de-duplication and dead-letter routes are ready-made nodes.

Workflows

Real workflows. From this field.

5 workflows from our test suite that teams in this field start with — ready to adapt to your data.

100 million property sales are loaded once; the revenue-by-town query then answers in milliseconds.

PROTOR · Revenue by town over 100 million salesrunning
CSV SubscriberCSV.SubscriberSQL QueryETL.SQLQueryCLI ProcessorETL.CLIProcessor
  1. CSV Subscriber · SQL QueryCSV.Subscriber · ETL.SQLQuery
  2. CLI ProcessorETL.CLIProcessor
nodes
src → CSV.Subscriber
sql_cold → ETL.SQLQuery
out → ETL.CLIProcessor
100 M property sales · revenue by town in 3.5 ms (warm) · measured

Real workflows and node types; the designer view is illustrated. Measured values come from our end-to-end test runs.

Find your role. 8 of them.

Data/analytics engineering teams fighting cloud warehouse bills

Stop paying a cluster for laptop-sized queries.

Finance & trading risk analytics

Risk numbers you can reproduce on demand.

Retail, e-commerce and quick-commerce operations

City-scale inventory analytics on one box.

System integrators and consultancies building data platforms for clients

One engine to deliver, audit and hand over.

ISVs and SaaS vendors needing an embedded analytics / ETL engine

Built into your product.

AI/ML teams

Reproducible data prep at memory speed.

Insurance analytics

Reproducible reserves, run in minutes.

Mid-size companies without a data team

Replace the spreadsheet chain, keep the data.

Your pilot. Three steps.

1

Your data shape

Tell us what data you have, where it lives and what the result must be.

2

A scoped pilot

On your data and your hardware, with the success measure written down first.

3

A clear decision

You see the result against that measure — and keep every workflow and record.

Beta · design partners wanted

Bring us your hardest data problem. Let's solve it together.

A short pilot on your data and your hardware. We agree on what success looks like before we start.

Start a pilotSee the numbers