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DataNexx

Proprietary data for AI

Real-world industrial data for the next generation of AI.

We connect organizations that own unique laboratory, manufacturing, scientific and industrial-process data with AI companies searching for high-quality proprietary datasets.

Evaluate Your Data Request a Dataset
  • No raw data required for an initial assessment
  • Confidentiality-first
  • Global partnerships
Record MT-0418 · TensileIllustrative
  1. 01Physical world
    Specimen · Al 6061-T6 · Ø 12.5 mm · lot 22-B
  2. 02Measurements
    UTS 312 MPa
    Elong. 11.8 %
  3. 03Expert decision
    Engineer review: necking before fracture, consistent with ductile failure
  4. 04Verified outcome
    Pass · ductile fracture
  5. 05Structured data
    { "alloy": "6061-T6", "uts_mpa": 312, "failure": "ductile", "result": "pass" }
  6. 06AI
    Training · evaluation · reinforcement learning
  1. 01 →The physical worldSamples, materials, machines, batches
  2. 02 →MeasurementsInstruments, sensors, test rigs
  3. 03 →Expert decisionsTechnicians, engineers, operators
  4. 04 →Verified outcomesPass / fail, failure mode, result
  5. 05 →Structured dataDocumented, de-identified, licensed
  6. 06 AITraining, evaluation, RL, agents

Why this data matters

Train AI on what actually happened.

The most valuable datasets are rarely just documents. They record how something played out in the physical world — and who decided what along the way.

  1. Inputs
  2. Measurements
  3. Expert decisions
  4. Corrections
  5. Verified outcomes
AI models can learn from text. Industrial AI increasingly needs data describing how the real world behaves.

Worked example · Materials

Illustrative

  1. 01Material sample
  2. 02Stress test
  3. 03Instrument measurements
  4. 04Engineer review
  5. 05Pass / failure mode
  6. 06AI training dataset

Real world, not synthetic

Data AI cannot scrape from the public internet.

Public and synthetic data each have a role. Measured data from real operations adds what neither can: ground truth from physical processes and the experts who ran them.

Public web data

Strong at

Broad language and general knowledge

Limitation

Rarely contains instrument readings linked to verified outcomes

Synthetic data

Strong at

Scale, coverage and controllable edge cases

Limitation

Only as faithful as the simulator or model that produced it

Proprietary measured data

Strong at

Ground truth from real processes, expert decisions and results

Challenge

Scattered across organizations that never prepared it for AI

Data categories

Where valuable data already exists.

Laboratories, plants and test facilities have been recording measurements, decisions and outcomes for years — usually for compliance or quality, never for AI.

All categories

Characteristics buyers value

What makes industrial data valuable?

Buyers look for a combination of these characteristics. Few datasets have all of them; the strongest have several, linked across a single workflow.

SPEC 01
Proprietary
Data not already widely available online.
SPEC 02
Measured
Real observations from physical-world processes.
SPEC 03
Expert-generated
Contains technician, scientist, engineer or operator judgment.
SPEC 04
Verified
Includes known results or ground truth.
SPEC 05
Longitudinal
Years of history can reveal rare events and edge cases.
SPEC 06
Connected
Data spanning multiple stages of a workflow may be more useful.
SPEC 07
Multimodal
Structured records, images, documents, signals and measurements together.
SPEC 08
Rights-cleared
Clear provenance and licensing rights.

For data owners

Your historical data may be more valuable than you think.

Years of tests, measurements and production records could have a second life. We help you find out — without sending raw data or disrupting operations.

  • Confidential, high-level initial assessment
  • Rights, privacy and customer-confidentiality review
  • Preparation, documentation and buyer matching
Discover What Your Data Could Be Worth

For AI companies

Access data the internet doesn't have.

Source proprietary ground-truth data from the physical world: real measurements, expert decisions and verified outcomes, with documented provenance.

  • Sourcing to your specification, not a fixed catalog
  • Dataset cards and provenance documentation
  • License scope defined contractually
Source Proprietary Data

For data owners · How it works

From dormant records to a licensed dataset.

Seven steps, each with an exit. Most of the early work happens without any of your data leaving your systems.

Full process
  1. STEP 01

    Describe your data

    Share high-level information about what you hold. No raw data is required at this stage.

  2. STEP 02

    Dataset assessment

    We evaluate whether the data matches what AI developers are actively looking for.

  3. STEP 03

    Rights & privacy review

    Owning data and having the right to license it are not the same thing. We work through the difference with you and appropriate specialists.

  4. STEP 04

    Dataset preparation

    Where a dataset qualifies, we help turn operational records into a documented, licensable asset.

  5. STEP 05

    Buyer matching

    We match qualifying datasets with organizations that have a relevant need.

  6. STEP 06

    Licensing

    Commercial terms are negotiated per transaction. Depending on the dataset and the buyer, structures may include:

    Not every structure is available for every dataset or buyer.

  7. STEP 07

    Revenue

    If a transaction is completed, you receive the compensation agreed in the license.

    No sale is guaranteed. Whether a dataset licenses depends on buyer demand, rights and quality.

For AI companies

Tell us the data your model needs.

You tell us what your model needs. We find organizations that produce it — and help them prepare it responsibly.

Dataset specificationExample
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
  1. 01

    Specify

    Tell us what your model needs: domain, structure, modalities, scale, time period, rights and intended use.

  2. 02

    Source

    We search our supplier network and approach organizations that produce matching data — rather than only offering a fixed catalog.

  3. 03

    Qualify

    Candidate datasets are assessed for structure, quality and rights. You review anonymized descriptions and documentation before anything moves.

  4. 04

    License

    Terms, scope and permitted uses are set out contractually. Delivery follows only with the data owner's authorization.

Request a Dataset

Example dataset profiles

The shape of a strong dataset.

Fictional profiles that show the kind of data we look for: linked records, long histories and verified outcomes. They are not datasets currently available.

  • EX-01 · DATASET PROFILEIllustrative

    Food Quality Dataset

    History
    8 years
    Tests
    1.8M
    • Chemical + microbiological measurements
    • Production batch linkage
    • Pass / fail outcomes

    Potential applications

    Industrial QA · Food science AI · Anomaly detection

  • EX-02 · DATASET PROFILEIllustrative

    Materials Testing Dataset

    Tests
    320,000
    Test types
    3
    • Tensile, compression and thermal measurements
    • Material composition
    • Failure classifications
    • Engineer-reviewed outcomes

    Potential applications

    Materials science · Engineering models · Physical reasoning

  • EX-03 · DATASET PROFILEIllustrative

    Manufacturing Process Dataset

    History
    12 years
    Signals
    Machine telemetry
    • Production settings
    • Defect records
    • Corrective actions
    • Final QC results

    Potential applications

    Predictive maintenance · Process optimization · Industrial agents

Illustrative example only. These profiles are fictional and do not describe datasets currently available.

Trust & compliance

Data licensing without losing control.

Your data stays yours until you decide otherwise. We assess before anything is shared, and we treat rights and privacy as conditions of a transaction — not afterthoughts.

No raw data for an initial evaluation

The first assessment uses descriptions, schemas and counts — not your records.

Nothing moves without authorization

Data is not transferred to buyers without your explicit authorization and an agreed license.

Ownership is not the same as licensing rights

Customer contracts, consents and confidentiality terms can limit what may be licensed, even for data you hold.

Specialist review where it is needed

We work with data owners and appropriate legal and compliance specialists to determine what can be licensed.

Datasets may require

  • Contractual review
  • Customer-consent review
  • Anonymization
  • De-identification
  • Removal of restricted information
  • Export-control review
  • Cross-border data review

Removing names alone does not guarantee anonymization. Combinations of dates, locations, product codes or rare events can re-identify people or customers, so de-identification is planned dataset by dataset.

DataNexx does not provide legal advice. Data licensing transactions may require independent legal, privacy, regulatory, or export-control review.

Not every dataset is a fit

Data we generally do not want.

Datasets involving these categories may require additional review or may not be eligible. Telling you early is part of protecting you.

  • Personal consumer data
  • Patient-identifiable medical information
  • Payment-card information
  • Passwords or authentication data
  • Restricted defense information
  • Export-controlled technical information
  • Government-classified information
  • Information the seller has no right to license

Differentiation

Real-world ground truth. Nothing else.

We specialize in discovering and commercializing proprietary datasets produced through real scientific, laboratory, manufacturing and industrial activity.

  • NOTA generic dataset marketplace

    We source to specification and prepare each dataset with its owner.

  • NOTA web-scraping company

    Our datasets come from the organizations that generated them, with their authorization.

  • NOTA synthetic-data generator

    We work with measured data. It complements synthetic data rather than replacing it.

  • NOTA consumer data broker

    We do not trade in personal information about consumers.

  • NOTAn annotation outsourcer

    The expert labels already exist — they were made by the people who did the work.

Own valuable data?

Find out whether your historical data could qualify for AI licensing.

A confidential, no-raw-data assessment of your organization's laboratory or industrial records.

Evaluate Your Data

Building AI?

Tell us the proprietary data your model needs.

Specify the domain, structure, modalities and rights. We source from organizations that produce it.

Request a Dataset