Export and prepare enterprise data for AI licensing.
Start with a permitted scope. Extract records and their relationships, check coverage, remove sensitive information, then document quality and licensing rights. These 20 platform guides and 10 preparation guides explain the technical work between an export and a commercially assessable dataset.
Choose a source, then prepare it for review
Exports support backups, migrations and internal analysis. Licensing for AI development requires a separate rights and privacy review. Technical access alone does not establish permission to license employee, customer or third-party material.
New to the process? Read how to prepare enterprise data, how licensing works, and the commercial guide to company data.
30 guides
Collaboration
How to Export Google Workspace Gmail and Drive Data
Scope a Google Workspace organization export, inventory Gmail and Drive outputs, preserve message and document context, and prepare a reviewed enterprise dataset.
Read the guide → CollaborationHow to Export Microsoft Teams Chats and Channels
Plan Microsoft Teams exports with Graph, preserve chat and channel relationships, reconcile duplicate messages, and document retention and licensing restrictions.
Read the guide → CollaborationHow to Export Outlook Email Archives and Metadata
Export Outlook email to PST, understand local synchronization and client differences, and preserve message headers, attachments and thread context for AI datasets.
Read the guide → CollaborationHow to Export Slack Messages, Threads and Metadata
Export Slack channel messages and thread context, understand JSON archives and file links, and prepare a permissioned dataset for potential AI licensing.
Read the guide → CollaborationHow to Export Zoom Transcripts, Recordings and Metadata
Download Zoom cloud recordings and VTT transcripts, preserve meeting and segment context, and review speaker permissions, transcript accuracy and media completeness.
Read the guide →CRM
How to Export HubSpot Contacts, Deals and Activities
Export HubSpot CRM properties and associations, collect activities separately, and preserve pipeline definitions and deal context for a reviewed enterprise dataset.
Read the guide → CRMHow to Export Salesforce CRM Records and Outcomes
Export Salesforce object data, connect opportunities to activities and outcomes, and distinguish backup CSV files from a prepared CRM dataset for AI licensing.
Read the guide →Customer support
How to Export Freshdesk Tickets and Conversation Histories
Distinguish Freshdesk ticket CSV from account exports, collect conversation records with an approved API, and preserve replies, notes and support outcomes.
Read the guide → Customer supportHow to Export Intercom Conversations and Support Records
Export Intercom conversation content separately from reporting CSV, preserve conversation parts and customer relationships, and check long-thread completeness.
Read the guide → Customer supportHow to Export Zendesk Tickets and Conversation Histories
Choose Zendesk JSON or incremental API exports for support conversations, preserve ticket events and public-note distinctions, and validate resolution histories.
Read the guide →Engineering
How to Export GitHub Repositories, Issues and Pull Requests
Back up GitHub Git history, collect issues and pull requests separately, account for LFS and discussions, and prepare rights-reviewed engineering datasets.
Read the guide → EngineeringHow to Export GitLab Repositories, Issues and Merge Requests
Use GitLab project exports with version-aware exclusions, preserve merge-request and issue context, and review source history and licensing rights before AI preparation.
Read the guide → EngineeringHow to Export Jira Issues, Comments and Change Histories
Export Jira Cloud issues as CSV, collect paginated changelogs where needed, and preserve workflow transitions, comments and attachment references for AI datasets.
Read the guide → EngineeringHow to Export Linear Issues, Projects and Workflow Data
Export Linear workspace or issue-view CSV, preserve issue and project relationships, and distinguish snapshots from comments, attachments and workflow history.
Read the guide →Knowledge
How to Export Confluence Knowledge and Document Histories
Choose Confluence Cloud space export formats, understand comment and visibility limits, and preserve page ancestry, version evidence and attachments for datasets.
Read the guide → KnowledgeHow to Export Notion Documentation and Knowledge Bases
Export Notion pages, databases and workspace content as HTML or Markdown and CSV, preserve page relationships, and check comments, assets and knowledge quality.
Read the guide → KnowledgeHow to Export SharePoint Documents, Lists and Metadata
Collect SharePoint document files and list metadata through distinct export methods, preserve item relationships, and document version and permission gaps.
Read the guide →Operations
How to Export PostgreSQL Records for Dataset Preparation
Use PostgreSQL COPY and pg_dump appropriately, export a scoped CSV from psql, and preserve database relationships, consistent snapshots and dataset provenance.
Read the guide → OperationsHow to Export SAP Operational and Transactional Records
Plan version-specific SAP exports, use approved list and spreadsheet workflows, and preserve transaction keys, currencies and operational relationships for AI datasets.
Read the guide → OperationsHow to Export ServiceNow Incidents and Workflow Records
Export ServiceNow list data or Table API records, preserve reference keys and journal context, and validate incident and service-request datasets for AI preparation.
Read the guide →Preparation
How to Anonymize Enterprise Data for AI Training
Plan enterprise anonymization around identifiability and utility, distinguish pseudonymization from anonymity, and review residual risk before dataset release.
Read the guide → PreparationHow to Convert CSV Data into JSONL for AI Training
Convert CSV to validated JSONL with a streaming Python example, preserve identifiers and multiline text, and define types and null handling before model preparation.
Read the guide → PreparationHow to Create a Dataset Manifest for AI Licensing
Create a versioned dataset manifest with file hashes, schemas, counts, coverage and review status, and separate release metadata from restricted provenance evidence.
Read the guide → PreparationHow to Deduplicate Large Text Datasets
Design exact and near-duplicate checks for enterprise text, preserve source lineage, scale hash-based grouping, and prevent duplicate families leaking into evaluation.
Read the guide → PreparationHow to Document Dataset Provenance and Licensing Rights
Trace enterprise datasets from source exports through transformations, maintain a rights register, and document unresolved ownership, privacy and third-party restrictions.
Read the guide → PreparationHow to Evaluate Enterprise Dataset Quality
Evaluate enterprise datasets with measurable completeness, consistency, context and label checks, build reviewed samples, and report limitations before AI licensing.
Read the guide → PreparationHow to Prepare Enterprise Data for AI Training
Build a scoped enterprise dataset from verified exports, preserve relationships, review sensitive information, and document quality and rights before AI licensing.
Read the guide → PreparationHow to Preserve Conversation Context for AI Training
Reconstruct message threads with stable IDs, speaker roles and chronology, flag missing parents and attachments, and avoid context leakage when preparing conversations.
Read the guide → PreparationHow to Remove Personally Identifiable Information from Datasets
Build a PII detection and redaction workflow for enterprise text, metadata and attachments, measure missed entities, and preserve useful context under privacy review.
Read the guide → PreparationHow to Structure Customer Support Conversations into AI Datasets
Turn support tickets into role-aware conversation examples, separate public replies and internal notes, validate resolution labels, and group related cases for evaluation.
Read the guide →What information helps an initial dataset assessment?
Describe the source, coverage, context and review work still needed. This fictional example shares no raw records and makes no claim of completed rights clearance.
{
"source": "Support conversations",
"languages": [
"English",
"Japanese"
],
"coverage": "2024–2026",
"volume": "Approximately 8,000 tickets; completeness not yet reconciled",
"context": "Replies and outcomes; attachments excluded",
"review_status": "Privacy and licensing-rights review pending"
}Have a proprietary dataset?
Find out whether it may be suitable for AI licensing. Start with a high-level description of the source, date range, volume, rights status, and preparation work still needed.
Assess dataset readinessUse the existing buyer application when ready. Share only authorized information. Do not submit raw confidential records, credentials, or personal data in the initial assessment.