Marketplace/Asset #912LISTED

AI data asset · anonymous listing

#912

ZendeskJiraGitHubSlackSnowflake
AI Data Asset Score11 interested parties
79/100
Potential data value
Assessment available after diligence
Licensing readiness
REVIEW REQUIRED
Preferred structure
Non-exclusive, de-identified derivatives
Data room

Seller identity is never disclosed at this stage. Introduction requires buyer qualification, seller approval and executed confidentiality.

Data overviewListed 2026-07-01
Industry
SaaS
Region
European Union
Company size
100–500 employees
Years operating
9+
Historical data
7 years
Estimated records
1.1M+ tickets, issues and deploys
ERP
NetSuite
CRM
HubSpot
Approx. employees
260
Data types3 categories
  • Support Tickets
  • Engineering Workflows
  • Customer Relationships
Workflow familiesDepth: excellent
  • Tier-1 to tier-3 escalation
  • Bug triage to release
  • Incident response
  • Renewal risk escalation

Cross-system connectivity

Zendesk → Jira → GitHub → Snowflake

Score composition79 / 100

Volume

Over a million linked tickets, issues and deploys.

92

weight 12

Historical depth

Seven years, with a support-platform migration in year three.

74

weight 12

Workflow complexity

Escalation paths are well structured but shallow.

72

weight 12

Cross-system connectivity

Ticket-to-issue-to-commit chains are traceable.

90

weight 11

Uniqueness

Support corpora are comparatively well represented.

64

weight 10

Data quality

Consistent taxonomy since the migration.

88

weight 9

Outcome labels

Resolution, reopen and satisfaction outcomes present.

90

weight 9

Industry value

Steady demand for support and coding agent evaluation.

78

weight 8

AI-agent relevance

Ticket-to-code resolution is a canonical agent task.

86

weight 8

Licensing readiness

Customer contracts need confidentiality review.

66

weight 5

Privacy / de-identification complexity

GDPR scope; customer contacts in ticket bodies.

58

weight 4

Why this asset scored 79

Strong linkage is the defining feature here: a customer report can be followed to the issue and the commit that resolved it, which is exactly the chain coding and support agents are evaluated against. Uniqueness is the largest deduction, since support corpora are comparatively well represented on the open market, and GDPR scope adds de-identification work on customer contacts inside ticket bodies.

Potential AI applications
  • Support deflection
  • Vertical AI agents
  • Evaluation benchmarks
Data rights & provenanceNot a legal determination

Ownership

Company-owned tenants.

READY

Source systems

Zendesk, Jira, GitHub, Snowflake.

READY

Third-party data

Customer-submitted attachments present.

REVIEW REQUIRED

Customer / client data

Business contact data in ticket bodies.

REVIEW REQUIRED

Employee data

Agent and engineer identities.

REVIEW REQUIRED

Potential PII

GDPR-scope personal data.

REVIEW REQUIRED

Contract restrictions

Customer MSAs include confidentiality terms.

REVIEW REQUIRED

Licensing permissions

Pending rights review.

NOT VERIFIED

Retention period

Seven years retained.

READY

Geographic restrictions

EU processing preferred.

REVIEW REQUIRED

Intended AI use

No customer-identifiable outputs.

REVIEW REQUIRED

De-identification requirements

Contact and attachment scrubbing.

REVIEW REQUIRED

Status indicators reflect seller-reported information and SentryRights screening only. No asset is represented as legally licensable until rights and privacy diligence is completed with the seller and, where applicable, its counsel.

Demo data — illustrative sample listings, not live marketplace records

Data readiness
Volume
HIGH
Historical depth
MODERATE
Workflow complexity
MODERATE
Cross-system connectivity
HIGH
Industry uniqueness
MODERATE
Data quality
HIGH
Outcome labels
HIGH
PII risk
MODERATE
De-identification difficulty
MODERATE
Licensing readiness
REVIEW REQUIRED
Introduction process
  1. 1

    Buyer submits request

    Identity, funding stage and entity are verified before the request moves.

  2. 2

    Buyer provides intended use

    Specific model, application and downstream distribution limits stated in writing.

  3. 3

    Platform reviews buyer

    SentryRights qualifies the buyer and screens the intended use against seller preferences.

  4. 4

    Seller receives request

    Seller sees the buyer profile and intended use while remaining anonymous.

  5. 5

    Seller approves or declines

    No identity is disclosed without an explicit seller approval.

  6. 6

    NDA / confidentiality

    Mutual confidentiality executed through the platform.

  7. 7

    Identity revealed

    Counterparties are introduced only at this point.

  8. 8

    Data diligence begins

    Rights review, privacy review and de-identification are coordinated by SentryRights.