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“For product teams whose analytics feature costs more than it earns”

Built into your product.

The same engine ships inside an on-premises build and a SaaS tenant.

Start a pilotHow it runs
The problem
  • Our analytics feature costs us more cloud money than the plan earns.
  • Customers on-premises or in-region want it where their data is.
  • Embedding a big engine means embedding its operations and its licence terms.
With PROTOR
  • The same engine ships inside an on-premises build and a SaaS tenant.
  • Offer your customers reproducible, audit-ready reports as a feature.
  • Win regulated and public-sector customers.

From today to the first result.

What you do today

a cloud analytics service that the vendor pays for

The analytics feature costs more cloud money than the plan earns, and customers who need it on-premises or in-region wait for a deployable build.

With PROTOR

One engine for the SaaS tenant and the on-premises build, running on your customer's hardware without a per-query cloud bill: 1 billion rows grouped in 32.1 ms on one notebook.

Your first step

Put the engine behind one existing report endpoint and compare output and cloud cost.

one report
How it runs

Revenue by town over 100 million sales. Step by step.

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
  1. CSV SubscriberReads the sales CSV in 1M-row batches with a fixed schema.
  2. SQL QueryRuns the query as soon as the data is loaded.
  3. CLI ProcessorPrints the town table.

A real workflow from our end-to-end test suite; the designer view is illustrated.

What you get. From the first pilot.

The same engine ships inside an on-premises build and a SaaS tenant.

Offer your customers reproducible, audit-ready reports as a feature.

Win regulated and public-sector customers.

Why your tools fall short.

×

BI-embed vendors charge per user or per render and lock the UX.

×

Spark/warehouses are not embeddable in an on-premises product.

×

DuckDB is embeddable but lacks orchestration, audit and streaming.

What drives it
  • Customer demand for in-product analytics
  • Cost-of-goods pressure
  • On-premises/sovereign deployment demands
  • Time to market

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.

Related roles in Cost & reproducibility.

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.

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.

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