Data a model can actually use

Runix Pipeline · In development

The unglamorous half of every AI project: getting raw, duplicated, half-structured source material into something a model reads without choking. Built as a managed pipeline, delivered with a quality report.

Become a design partner Read how we think about it How an engagement works

Status: in development. Runix Pipeline is being built alongside a small number of design partners, and is scoped per engagement rather than sold as a self-serve product. If the stages below match a problem you have now, that is exactly the conversation we want.

Six stages, each one auditable

Every stage produces something you can inspect, so a bad answer downstream can be traced to the record that caused it.

Ingest

Documents, exports, databases and APIs, with the parsing trade-offs written down rather than hidden in a script.

Clean & dedupe

Format normalisation, noise removal, and deduplication at exact, near and semantic levels — with counts you can check.

Structure

LLM-assisted extraction into a schema you define, validated on the way out so malformed records fail loudly.

Mask

Personal data identified and masked before it reaches a model or a training set, failing closed when detection is uncertain.

Report

A quality report per delivery: coverage, duplication rates, extraction confidence, and what was dropped and why.

Deliver

Files, a database, or an endpoint — batch or continuous, whichever your downstream actually consumes.

Nobody's data is generic

Cleaning pipelines fail on the specifics: the one export with a broken encoding, the near-duplicates that are not duplicates, the field that means two different things in two systems. We scope those with you rather than pretending a wizard handles them.

That is also why it is priced per project or volume rather than per seat — you are buying the judgement calls, not a dashboard.

What an engagement looks like

Starts withA sample of your real dataNot a questionnaire

You get backA scoped planStages, trade-offs, and what we will not attempt

DeliveryBatch or continuousFiles, database, or endpoint

Every deliveryShips with a quality reportIncluding what was dropped and why

PricingPer project or volumeQuoted before work starts

Common questions

Why an engagement, not a self-serve tool?

Because the hard part is never the transform — it is deciding what "clean" means for your data, and that judgement is different every time. We start from a sample of the real thing and come back with a scoped plan, including the parts we think are not worth doing.

What happens to the data we send?

It is processed to do the work you asked for — not used to train models, not sold. The full statement is in the Privacy Policy, and the operational posture in Security.

How is it priced?

Per project or by volume, quoted before any work starts. You see the number and the scope together, so there is nothing to reconcile afterwards.

How do we start?

Pipeline is being built with design partners, so we take on a small number of engagements at a time. Send a sample and what you need out of it — become a design partner and you get a scoped plan back.

Have a pile of data and a deadline?

Send a sample and what you need out of it. You get a scoped plan back, including the parts we think are not worth doing.

Become a design partner