Delovantage AI Labs / Research
Today we ship one thing: consent-verified real-world AI training data, captured to specification. This page is what we are building toward, and exactly how far each program has actually gone.
[ 01 ]Thesis
01
Trained people record real tasks in live environments, with documented consent and synchronized sensors. This is the part we do commercially today.
02
Annotated multimodal data is what lets a model understand how humans actually handle objects, spaces, and edge cases. We are studying where this moves results.
03
Systems that perceive and act in the physical world are the reason the data matters. That is the direction, and we will publish it when there is something to show.
[ 02 ]Programs
3 programs · 1 shipping
Consent-verified multimodal data captured by trained people in live working environments, then annotated and licensed to specification.
Evidence
21 delivery formats, 10 capture industries, 12 annotation types, 4 licensing paths — all listed on the home page with sample output.
Research into which parts of robot autonomy improve most directly from human-captured real-world data.
Open question
Which layer of robot autonomy does our data advantage unlock first — perception, world modelling, or manipulation policy?
What we are testing
Internal evaluation of our own capture and annotation output against published embodied-AI baselines, to find where real-world human data changes results and where it does not.
Early thinking on the hardware we would need to own in order to capture better data than we can buy off the shelf.
Open question
Would a purpose-built wearable capture rig produce materially better training data than the commercial hardware we use today?
Built so far
Nothing. This is a direction, not a product.
[ 03 ]What we publish, and when
No launch dates, no renders presented as products, and no capability claimed before it exists. If a program is quiet here, it is because there is nothing honest to show yet.
[ 04 ]Open questions
Where does human-captured real-world data beat simulation, and where is simulation simply cheaper and good enough?
How much of a robot stack can be improved by better data alone, before architecture becomes the limit?
Which capture modality is most underserved today relative to what model teams actually ask us for?
What would we have to build ourselves because no vendor sells it?
The fastest way to work with us today is the data. Tell us what your model needs to see and we will tell you honestly whether we can capture it.
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