An applied research lab for visual intelligence
Build intelligence for the visual world
Data infrastructure for video, built to train and deploy visual AI.
LatestVideoDB leads video retrieval benchmarks across 9,800 queriesBacked and supported by
UC Berkeley
IIIT BangaloreMango CapitalStrange VenturesUntapped CapitalOVO FundUC BerkeleyIIIT Bangalore
01 /What we do
We turn video into data you can use.
We investigate where your model fails, find the data that fixes it, and bring scalable infrastructure and the expertise to go with it, so your team builds intelligence from video faster.
All productsProbe the test runs.
Test runs are annotated and indexed as they finish. Search agents go through each run, pull out the moments where the model failed and group them by cause, so the team sees which failure to work on first.
Read the lab postFind the episodes you already have.
Ask across your in-house datasets in plain language: same task, objects and lighting. Results come back as ranked episodes, with frames.
VideoDB leads search benchmark across 9,800 queriesConnect more sources when the archive runs out.
When the archive does not cover a failure, we bring in more: partner datasets, targeted collection, or live runs and cameras streamed into the same pipeline.
Realtime ingestionAnnotate, with human QC.
Any model runs as an analyzer over the episodes. Low-confidence labels go to human reviewers with the clip attached. 100,000 hours annotated in a few days for one customer.
VideoDB leads on WGO-BenchRetrain and test again.
The new samples go into the next training run. The updated model runs the same tests on the same infrastructure, and the next round of runs is searchable as soon as it finishes.
See the video primitives
02 /Research
Benchmarks, papers and engineering notes from the lab.
Most of what we publish is measured on public benchmarks, with the numbers included.
Papers and notes
Search over the Visual World
Technical report. Recall@1 of 73.1% against 65.8% for a commercial video-native engine, over 9,834 queries from four public datasets.
Aug 8, 2026
What video retrieval benchmarks taught us about ground truth
A manual review of MSR-VTT, MSVD, VATEX, DiDeMo and QVHighlights: where the labelled window is narrower than the clips that match the query.
Jul 24, 2026
Can a VLM annotate a robot run?
All 100 WGO-Bench videos re-annotated. A VLM pipeline reaches 28.9% semantic F1 at IoU 0.5, against a 19.9% public baseline.
Sep 8, 202603 /Talk to us
The next wave of AI will understand the visual world.
10k+ developers on the platform.
250+ TB processed.
To see is to know.