Solutions · Data preparation & curation

Raw data, cleaned and ready to label

Ingest straight from your cloud storage, clean and convert it, merge the metadata that gives it context, and pre-label it with models — so people spend their time correcting, not starting from zero. Sandia cut screening-data prep from eight weeks to three.

Multimodal Video Image Audio Text

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Hands on a laptop showing a grid of image and video thumbnails, with two external drives and a cable on the desk

The problem

Why this is hard to do well

Messy inputs

Mixed formats, duplicates, corrupt files, and inconsistent naming — before a single label is drawn.

Context lives elsewhere

Metadata, sensor readings, and companion files sit in other systems, so annotators label blind.

Weeks lost up front

Preparation is often the slowest part of a data program, and it's rarely anyone's full-time job.

How MLtwist does it

Platform and people, together

Our platform does the repeatable work; our team, yours, or both handle the judgment calls. Every version ships with a Data ID Card.

Ingest in place: Read directly from Google Cloud Storage, Amazon S3, or Azure Blob. No local copies.
Clean, split, and convert: Deduplicate, split, resize, and transcode files — video to web-ready formats, large images to tiles.
Merge context: Metadata and companion files such as data cards and prior labels travel with each file.
Pre-label with models: Foundation models draft labels so people correct instead of starting from zero.
Curate into datasets: Organize files into datasets and batches, and send only what's worth labeling.