VECTORIO
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“For ML teams whose data prep is slower and less repeatable than their models”

Reproducible data prep at memory speed.

Feature tables and dedup passes finish before the GPU idles.

Start a pilotHow it runs
The problem
  • Data prep eats my week and every rerun gives slightly different data.
  • AI pilots are inflating the warehouse and cloud bill.
  • Proprietary documents can't leave our network for a vector pipeline.
With PROTOR
  • Feature tables and dedup passes finish before the GPU idles.
  • Dataset lineage for each model run.
  • Confidential RAG corpora stay on-premises.

From today to the first result.

What you do today

scripts and a warehouse for data prep

Data prep takes the week, every rerun yields slightly different data, and AI pilots inflate the warehouse bill.

With PROTOR

103 million rows through a 21-stage prep workflow in 24.4 s, with the same data on every rerun and a lineage record per model run.

Your first step

Move one dedup or feature-prep pass onto a notebook and keep the training stack as it is.

one afternoon
How it runs

A language model on your own hardware. Step by step.

A local model answers a batch of prompts — no network, and the data stays on the machine.

PROTOR · A language model on your own hardwarerunning
Prompt Source (CSV)CSV SubscriberLocal LLM ProcessorLocalLLM.ProcessorCLI OutputCLI Processor
  1. Prompt Source (CSV)CSV Subscriber
  2. Local LLM ProcessorLocalLLM.Processor
  3. CLI OutputCLI Processor
nodes
csv-prompts → CSV.Subscriber
llm-processor → LocalLLM.Processor
cli-output → ETL.CLIProcessor
local modelno network
  1. Prompt Source (CSV)Reads rows from a CSV file in batches.
  2. Local LLM ProcessorAnswers each row with a local language model.
  3. CLI OutputPrints the rows as a table in the terminal.

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

What you get. From the first pilot.

Feature tables and dedup passes finish before the GPU idles.

Dataset lineage for each model run.

Confidential RAG corpora stay on-premises.

Why your tools fall short.

×

Notebook scripts are not reproducible or auditable.

×

Cloud data-prep services cost per run and require data egress.

×

Spark is heavy for single-box prep.

What drives it
  • Experiment iteration speed
  • Reproducibility for model audits (EU AI Act context)
  • Data confidentiality
  • GPU time is expensive, so prep must not starve it

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.

ISVs and SaaS vendors needing an embedded analytics / ETL engine

Built into your product.

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