Scientific AI
Experimental records, including the experiments that failed.
Scientific models learn from methods and results. Internal laboratory and R&D records add what publications usually leave out: full parameters, instrument settings and negative results.
- 01 →The physical worldSamples, materials, machines, batches
- 02 →MeasurementsInstruments, sensors, test rigs
- 03 →Expert decisionsTechnicians, engineers, operators
- 04 →Verified outcomesPass / fail, failure mode, result
- 05 →Structured dataDocumented, de-identified, licensed
- 06 AITraining, evaluation, RL, agents
What matters
What this data needs to have.
- 01
Method detail
Preparation methods, instrument settings and controlled variables, not just conclusions.
- 02
Negative results
Failed and abandoned experiments that never reached a journal.
- 03
Expert interpretation
How scientists and technicians read the instrument output.
Typical data shapes
- Experiment parameters
- Instrument outputs
- Technician observations
- Verified results
How sourcing works
You tell us what your model needs.
We find organizations that produce it and help them prepare it responsibly.
- Industry
- Materials testing
- Desired records
- 100,000+ laboratory tests
- Desired structure
- Inputs + measurements + verified outcomes
- Modalities
- Structured data, images, documents, signals, video, audio
- Geography
- Any, or specific regions
- Time period
- 2015 – present
- Exclusivity
- Non-exclusive acceptable
- Rights requirements
- Commercial training rights, documented provenance
- Intended use
- Training, evaluation, RL, benchmarking, research
- 01
Specify
Tell us what your model needs: domain, structure, modalities, scale, time period, rights and intended use.
- 02
Source
We identify and approach organizations that naturally generate matching data — including data that does not yet exist as a packaged product.
- 03
Qualify
We evaluate whether candidate data can be licensed and whether it meets your structure and quality bar. You review anonymized descriptions and documentation before anything moves.
- 04
Prepare
We work with the data owner to de-identify, structure and document the data to your specification.
- 05
License
Terms, scope and permitted uses are set out contractually. Delivery follows only with the data owner's authorization.
Own valuable data?
Check your data's licensing potential.
A confidential, no-raw-data assessment of your organization's scientific, industrial or operational records.
Building AI?
You specify it. We source it.
Tell us the proprietary data your model needs — domain, structure, modalities and rights. We approach the organizations that produce it.