The data frontier grows faster than it is mined.
Most of what sits past the edge has no price, no index, and no way in. We are building the machine to Discover it, Sample it, Access it, and Route it.
Buying from catalogstakes months and leaks alpha.
In a catalog, alpha starts decaying the day it is listed. Every deal is still negotiated bilaterally, from scratch — and by the time it closes, the edge you paid for is already priced in.
- weeks to months per deal
- legal review before any sample
- utility unknown until after purchase
Connect once.Reach every dataset.
Access agents negotiate entry, authenticate, and pull a live sample before anything is bought — so utility is measured against your own unique criteria, ready for ingest. Integrations collapse from n × m to n + m, which is what makes the long tail economically viable.
- One autonomous run
- access negotiated in-loop
- utility measured before purchase
Built with the research community
Source, evaluate, and license data — at the speed of compute
Launch pipelines that autonomously discover, negotiate, and deliver data. One request creates many deals across many providers.
Enterprise integrations to optimize your data and compute spend
Purpose-built for AI labs, agent teams, and data providers. Each engagement is hands-on, scoped to your stack, and designed to deliver measurable outcomes.
Data value estimation
Estimate the marginal utility of data across your existing catalog and the Brickroad network. Know what's worth buying before you spend on compute.
Now Onboarding→Runtime data access
Procure data at runtime across your existing vendors, internal catalogs, and the datasets on the Brickroad network. One integration, every source.
Now Onboarding→Market intelligence
Benchmark pricing, deal comparables, and demand signals across the Brickroad network. Know what data is worth before you negotiate.
Now Onboarding→Building the data frontier
The multiplexer protocol and agent infrastructure are formalized in our published peer-reviewed research.
Croissant Tasks: Machine-Actionable Metadata for Reproducible ML EvaluationsarXiv
Croissant Tasks is a declarative metadata format that turns benchmarks and competitions into machine-actionable specifications. It enables conceptual reproducibility: verifying a scientific claim through an independently generated implementation rather than brittle source-code replication.
Making the Discrete Continuous: Synthetic RAW Augmentations for Low-Light Person DetectionCVPR 2026 Workshop
Real datasets are sparse and uneven, which makes it hard to evaluate vision models where it matters most. By synthesizing physically faithful low-light RAW samples, we can turn a discrete, long-tailed variable into a continuous, controllable one and fairly characterize pedestrian detection in the dark.
Croissant Baker: Local-First Metadata Generation for Governed ML DatasetsarXiv
Croissant has become the metadata standard for ML datasets, but generating it usually means uploading data to a public platform — impossible for clinical, government, and enterprise data. Croissant Baker generates validated Croissant metadata locally, directly from a dataset directory, reaching 97-100% agreement with ground truth across domains and scaling to MIMIC-IV's 886 million rows.
The Information FrontierEssay
A reductionist view of machine learning as a perpetual data refinery, and a re-calibration of its primitives. Why the information frontier is perpetually expanding, what physics says about ever collapsing it, and what it implies for the learning systems we build and study.
The Data Multiplexer for the Agent EconomyThesis
Formalizes the structural problem in data markets — n × m bilateral integrations — and introduces the multiplexer as a universal adapter that collapses integrations to n + m while optimizing min(Cd + Ct) subject to utility thresholds.
A Sustainable AI Economy Needs Data Deals That Work for GeneratorsNeurIPS 2025
Ruoxi Jia, Luis Oala, Wenjie Xiong, Suqin Ge, Jiachen T. Wang, Feiyang Kang, Dawn Song — formalizes the structural barriers preventing data generators from capturing fair value in the AI economy.
OpenML: Insights from 10 Years and More Than a Thousand PapersPatterns
A decade of OpenML, the open-source platform that turns machine-learning experiments into open, linked, and reusable knowledge. We look at the state of the ecosystem, how community-curated datasets, tasks, and benchmark suites have powered 1,500+ studies, and the lessons learned from building open-science infrastructure for ML.
Croissant: A Metadata Format for ML-Ready DatasetsNeurIPS 2024
Working with data is still a key friction point in machine learning. Croissant is a metadata format that creates a shared representation across ML tools, frameworks, and platforms — making datasets discoverable, portable, and interoperable. It is already supported across repositories spanning hundreds of thousands of datasets.
DMLR: Data-Centric Machine Learning Research — Past, Present and FutureDMLR Journal
Drawing on discussions at the inaugural DMLR workshop at ICML 2023, this editorial outlines why community engagement and infrastructure are essential to creating the next generation of public datasets — and charts a collective path to sustain them for scientific, societal, and business impact.
Stop sourcing. Start shipping.
The infrastructure layer for AI data procurement.