01 / MULTI-SYSTEM CONTEXT
Enterprise workflows
Email, messaging, documents, tickets, and business records. Context from how organizations actually operate.
RequestDecisionOutcome
Agents & enterprise reasoning
Proprietary datasets for AI labs
Proprietary data.Real-world depth.
Our inventory spans enterprise workflows, longitudinal healthcare, payments, and specialized domains. Available for model training, post-training, and evaluation.
Existing inventory. Dataset-specific licensing. Samples on request.
Data for the full model lifecycle
Pre-trainingPost-trainingEvaluation
01 / The inventory
Our current inventory.
Explore the categories in our current inventory. Request the dataset list for specific assets, coverage, available fields, and licensing scope.
02 / RECORDS ACROSS TIME
Longitudinal healthcare
Clinical records with context across a patient’s care history. Longitudinal depth, beyond a single encounter.
EncounterFollow-upCare historyOver time
Clinical reasoning & health models
03 / TRANSACTION-LEVEL ACTIVITY
Payments & transactions
Payment and transaction activity from real operating systems. Review the event structure and context available in each dataset.
event_id · event_type · timestamp
Related records · Sequence · Context
Financial reasoning & event modeling
04 / DOMAIN-SPECIFIC KNOWLEDGE
Specialized domains
Distinctive proprietary data from industry-specific systems and expert work. Depth that general-purpose corpora miss.
Domain recordsExpert context
Domain adaptation & expert tasks
Diagrams illustrate data structures, not customer records. Specific datasets, fields, permitted uses, and preparation are confirmed individually.
02 / For AI labs
Start with the capability.
Then examine the data.
A corpus, a set of workflows, or a domain evaluation. The right dataset depends on what you need the model to do.
Broaden what your
model learns from.
Assess private domain text and structured records as additions to your training mix. Review representative samples, coverage, and preparation before selecting a dataset.
- Non-public domain knowledge
- Coverage and quality review
- Dataset-specific training permissions
What to reviewPRE-TRAINING
- Input
- Domain text & structured records
- Coverage
- Domains, languages, dates, and source systems
- Quality
- Filtering, duplication, completeness, and context
- Scope
- Provenance, permitted uses, and delivery format
REVIEW FRAMEWORK / Not a dataset specification
03 / Licensing & delivery
Clear scope.
Before you commit.
Evaluate the data and the terms together. Provenance, privacy, and delivery are part of the discussion from the start.
Know what you’re licensing.
Review the origin, available documentation, permitted uses, and restrictions for the datasets you select.
Keep context. Define privacy.
Agree de-identification, fields, and quality requirements. Establish which relationships and sequences can be preserved.
Set the handoff requirements.
Define sample access, structure, format, and acceptance criteria before delivery. Additional task work is scoped separately.
A few practical questions
Do you already have these datasets?
Yes. These categories represent our existing inventory—not a promise to begin sourcing after an inquiry. The current dataset list provides specific assets, coverage, and access conditions. Availability and terms vary by dataset.
Can we review a sample before licensing?
Request the current dataset list or a representative sample. We agree access, confidentiality, and evaluation scope before sharing non-public records. The diagrams and downloadable field examples on this page are illustrative, not customer data.
What about healthcare and payments data?
Available fields, de-identification, permitted uses, and access requirements are reviewed dataset by dataset. This page does not imply access to personal identifiers, payment credentials, or unrestricted records.
Your next training input
See what’s in
the current inventory.
Share your target domain and intended use. We’ll start with the datasets already available.