Every company needs data. We built the infrastructure to get it.
Brickroad is the infrastructure layer for data procurement. Source, evaluate, and license data — at the speed of compute.
Trusted by 8,000+ researchers and developers
n × m. Bilateral negotiations. Months.
- 3–6 months per deal, $50k+ in transaction overhead
- Legal review alone consumes 4–8 weeks
- Utility unknown until after acquisition
- Long-tail data sources locked behind friction barriers
- No visibility into what the market actually needs
n + m. One adapter. Seconds.
- Full procurement lifecycle in 7 autonomous tool-use turns
- Per-deal transaction cost: ~$0.07
- Utility estimated before acquisition via sandbox evaluation
- Long-tail datasets become economically viable
- Demand signals visible across the entire network
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 1.5M+ 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 →Know what data is worth before you buy it
Estimate value, procure at runtime, and benchmark pricing across 1.5M+ datasets on the Brickroad network.
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.
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The infrastructure layer for AI data procurement.