Datachain

Use Case

View, Compare, and Reuse Multimodal Datasets in Place

Video, audio, 3D scans, time series, and tabular data, viewable directly from your buckets. No exports, no reformatting, no version drift.

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Trusted by startups and Fortune 500 companies

  • DeGould
  • Kibsi
  • Hugging Face
  • Mantis
  • Motorway
  • Inlab
  • Pieces
  • Sicara
  • UBS
  • SumerSports
  • UK Hydrographic Office
  • XP Inc.
  • Cyclica
  • Aicon
  • Billie
  • Papercup

Bring the view to your data

Manual exports separate what you see from the data and version behind it. DataChain closes that gap with file pointers, rich annotation types, and viewers built for each modality. Files stay in your buckets while a central registry serves typed datasets with full lineage. Teammates and agents work from the same versioned source, making the context layer for your unstructured data visible

One Place to Explore, Compare, and Reuse

Multimodal viewing

A viewer for every data type

Dataset DB holds file pointers; viewers read directly from your buckets. Inspect media and annotations in place, without exports, reformatting, or another tool.

  • View video frames, audio waveforms, time series, and structured tables alongside CT, MRI, and microscopy scans.
  • Overlay bounding boxes on images, segmentation masks on scans, and labeled segments on time series.
  • Browse, compare, and reuse dataset versions from the same registry.

Versioned registry

One source of truth for every version

Every chain deposits a typed dataset in the central Dataset DB. The UI shows its schema, lineage, and authorship alongside the data.

  • Browse by name, filter by schema, search by similarity, and follow lineage in either direction.
  • Compare versions side by side to see exactly what changed.
  • Share a pinned dataset version with a teammate instead of a stale CSV export.

Team collaboration

Share datasets, not file exports

Everyone reads the same typed, versioned source. Updates append versions instead of circulating files, so reusable datasets become a resource that grows with your team.

  • Review faster: open a version, inspect the chain that produced it, and validate before building on it.
  • See clear authorship and lineage on every record.
  • Reuse work across teams without making the dataset’s creators a bottleneck.

Add the missing data context layer to your object storage

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