Curate Training Data at Interactive Speed
Curation is a search problem.
Answering a simple question about your dataset shouldn’t require querying three systems and stitching results together.
Your raw data, embeddings, and metadata are fragmented across storage, vector databases, and analytics engines.
LanceDB brings it all into one table so you can explore, filter, and refine in real time.
Answering a simple question about your dataset shouldn’t require querying three systems and stitching results together.
Your raw data, embeddings, and metadata are fragmented across storage, vector databases, and analytics engines.
LanceDB brings it all into one table so you can explore, filter, and refine in real time.
Ask Questions, Not Write Pipelines
You spot something wrong. You want to know how many more examples look like it. That question shouldn't require three systems.
Find edge cases, remove bad data, and validate dataset distribution with a single query. Vector search, full-text search, and SQL filters all run on the same table.
No offline pipelines. No syncing embeddings. No cross-system joins.
Find edge cases, remove bad data, and validate dataset distribution with a single query. Vector search, full-text search, and SQL filters all run on the same table.
No offline pipelines. No syncing embeddings. No cross-system joins.
Get Real Results, Not Stitched Ones
When results come from different systems, they don’t match. Debugging that is on you.
Raw data, embeddings, and metadata live together. Every query returns the actual data, not pointers you need to fetch from somewhere else.
No second lookup. No mismatched results.
Raw data, embeddings, and metadata live together. Every query returns the actual data, not pointers you need to fetch from somewhere else.
No second lookup. No mismatched results.
Iterate on Datasets, Not Snapshots
The reflex when filtering is to copy. That copy goes stale. Someone forks it. Now you have seven versions of a petabyte dataset and no one knows which is correct.
Save subsets as views instead. Define training splits with queries, not pipelines.
No copies. No drift.
Save subsets as views instead. Define training splits with queries, not pipelines.
No copies. No drift.
One Dataset. Multiple Researchers. No Conflicts.
Without shared infrastructure, everyone makes their own copy just to move faster.
Multiple researchers can explore and curate the same dataset at the same time. Every change is versioned automatically. Branch for experiments, compare results, and roll back when needed.
No forks. No coordination overhead.
Multiple researchers can explore and curate the same dataset at the same time. Every change is versioned automatically. Branch for experiments, compare results, and roll back when needed.
No forks. No coordination overhead.
Use What You Curate, Immediately
The dataset you refine is the dataset you use.
Training, evaluation, and retrieval all run from the same table.
No exports. No rebuilds
Training, evaluation, and retrieval all run from the same table.
No exports. No rebuilds
