Issue trackers and engineering wikis from 11 wound-down SaaS companies
Summary
Complete issue tracker and wiki exports from 11 venture-backed US SaaS companies that shut down or sold their assets between 2022 and 2025, which we collect through each company's wind-down process. Each company's history runs from early product to final release, including roadmaps, specs, bug triage, sprint planning and architecture decisions. Staff and customer identities, hostnames and credentials are replaced with consistent placeholders. The archive is closed and will not grow.
Collected to order. We collect it from US companies for your order. Scope, volume and timing are confirmed in writing before you sign.
Pricing on request
Request scope and pricingAt a glance
- Planned scope. Volume, sources and timing are confirmed in writing before you sign.
- Records
- 2.6Missues and wiki pages
- Size
- 9 GB
- Tokens, estimated
- 1.3B
- Years
- 2012 to 2025
- Firms
- 11
- Owner profile
- 11 US venture-backed SaaS companies, 30 to 150 staff at peak, closed or sold between 2022 and 2025
- Industry
- Software
- Data type
- Code and engineering
- Headcount
- 30 to 150 staff per firm at peak
- Region
- United States
- Languages
- English
- Formats
- JSONL
- Updated
Sources
| System | Kind | Records |
|---|---|---|
| Jira | Projects and wiki | 2.36M issues, 9.8M comments |
| Confluence | Projects and wiki | 214K pages |
Good for
- training planning agents on how real teams break epics into stories and tasks
- evaluating spec-to-ticket breakdown against what teams actually filed
- studying how engineering priorities shift as a company runs short of runway
- retrieval over a whole company's design history from first spec to last release
Fields
| Field | Type | Description |
|---|---|---|
| firm_code | string | Pool member code |
| record_type | enum | issue or page |
| key | string | Issue key or page id |
| issue_type | enum | epic, story, task, bug or spike, null for pages |
| created_at | timestamp | Creation time |
| title | string | Issue summary or page title |
| body | string | Description or page text |
| status_history | array<string> | Each status with the date it was entered |
| parent_key | string | Parent epic or page |
| comments | array<object> | Author and text for each comment, in order |
Example record
{
"firm_code": "F03",
"record_type": "issue",
"key": "CORE-7731",
"issue_type": "bug",
"created_at": "2021-11-03T09:14:22Z",
"title": "Duplicate invoices when webhook retries after timeout",
"body": "When the payment processor retries a webhook after our 10s timeout we create a second invoice. Seen for [ACCOUNT_88] and [ACCOUNT_131] this week. Need idempotency on the event id.",
"status_history": [
"Open 2021-11-03",
"In Progress 2021-11-04",
"Done 2021-11-09"
],
"parent_key": "CORE-7605",
"comments": [
{
"author": "[PERSON_5]",
"text": "Added a unique index on provider_event_id and return 200 on conflict. Backfilled the dedupe for the 14 affected accounts."
},
{
"author": "[PERSON_12]",
"text": "Verified in staging with forced timeouts. Old duplicates are voided, not deleted, so finance keeps the trail."
}
]
}Licenses available
Exclusive
One buyer. Nobody else can license it during the exclusive term, usually 2 years.
Non-exclusive
Licensed to more than one buyer.
First look
One buyer gets 2 to 4 weeks to make an offer before anyone else sees the dataset.
What ships with it
- Datasheet: what it is, what it is good for, verified size, sources, years, format and limits.
- Provenance file: the owner shown by code, staff notice and consent status, exclusions, mapped to the EU AI Act Article 53 training-data summary and California AB 2013.
- Manifest with a hash for every file.
- QC report and de-identification report.
- Your own copy, with unique canary records.
How it is de-identified
Default exclusions removed, files flattened to text, identifiers found in three layers and replaced with consistent placeholders, and the result measured against a 99% bar for each identifier type in each source.