"Curated, rights-cleared training data from verified contributors. Buy premium datasets or earn by contributing."
TL;DR: Luel is a rights-cleared data marketplace + collection engine. Their difference is speed and edge cases: AI enterprises request datasets to spec, they mobilize a global contributor network, and deliver licensed, audit-ready data within days.
Frontier labs have hit a wall: public web data is tapped out, synthetic-only pipelines risk degeneration, and the next generation of models needs rights-cleared multimodal data that doesn’t exist at scale.
Companies are spending huge budgets on generic, low-signal datasets from legacy vendors
The internet’s “easy data” is largely exhausted, what’s left is low-signal, repetitive, or messy
Most datasets fail production requirements: unclear rights, weak provenance, missing consent, inconsistent metadata
The Solution
Luel delivers to-spec multimodal datasets with clean provenance:
Custom collections: you specify exactly what you need; they scope, recruit, QA, and deliver
Off-the-shelf licensing: completed collections become ready-to-license catalogue datasets (ranging from patient-doctor conversations in south Asia to gemstone manufacturing footage for robotics)
Rights trail included: built for procurement + compliance from day one (consent evidence, chain-of-title, QA logs)
How it works
AI teams submit a dataset spec (modality, scenario, instructions, devices, QA rules)
They post a listing and instantly match vetted contributors
Submissions run through multi-stage QA and are delivered within days.
🙏 If you’re training multimodal / robotics / speech models and need data, the team would love to talk. Intros to heads of data, applied research, or model training teams would be appreciate, reach the founders here.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
.png)
