Commercial water and fire restoration job files, 13 firms
Summary
Job files from 13 US restoration contractors working commercial losses: first-notice intake, moisture mapping and daily drying readings, scope notes, photo captions and the mail with property managers and adjusters. Drying readings are kept by day, room and material, so models can learn how a dry-out progresses and when it stalls. Residential claim files are excluded at collection, and people, properties, claim numbers and policy references are replaced with placeholders.
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
- 5Memails, drying readings, job files and documents
- Size
- 12.5 GB
- Tokens, estimated
- 1.4B
- Years
- 2017 to 2026
- Firms
- 13
- Owner profile
- 13 US restoration contractors doing commercial water, fire and mold work, 10 to 75 staff each, Gulf Coast, Southeast and Midwest
- Industry
- Field services
- Data type
- Field operations
- Headcount
- 10 to 75 staff per firm
- Region
- United States (Gulf Coast, Southeast, Midwest)
- Languages
- English, Spanish
- Formats
- JSONL, Parquet
- Updated
Sources
| System | Kind | Records |
|---|---|---|
| Job management system exports | Field operations | 61K jobs and 1.12M drying readings |
| Google Workspace mail | 3.6M messages | |
| Shared drives (scopes, reports, photo captions) | Shared files | 238K documents |
Good for
- training models to read drying logs and flag a dry-out that has stalled
- drafting scope narratives from moisture maps and photo captions
- evaluating extraction of loss details from first-notice intake notes
- summarizing long job threads for property managers and adjusters
Fields
| Field | Type | Description |
|---|---|---|
| job_id | string | Unique job id within the pool |
| firm_code | string | Placeholder for the contractor |
| loss_type | enum | water_cat1, water_cat2, water_cat3, fire_smoke or mold |
| property_type | enum | office_multi_tenant, retail, warehouse, school, hotel, multifamily_common and others |
| reported_at | timestamp | First notice time |
| intake_note | string | Note taken on the first call |
| readings | array<object> | Daily moisture readings by room and material, with the dry goal |
| status | enum | mitigation, drying, rebuild, closed |
Example record
{
"job_id": "RS13-JOB-0048215",
"firm_code": "[COMPANY_2]",
"loss_type": "water_cat2",
"property_type": "office_multi_tenant",
"reported_at": "2025-01-23T05:47:00-06:00",
"intake_note": "Sprinkler branch froze and split above suite 210. Water down through 2 floors. PM [PERSON_77] says riser shut at 5:20, needs crew asap, tenant opens at 8.",
"readings": [
{
"day": 1,
"room": "Suite 110 break room",
"material": "drywall",
"reading": 38,
"dry_goal": 12
},
{
"day": 3,
"room": "Suite 110 break room",
"material": "drywall",
"reading": 17,
"dry_goal": 12
}
],
"status": "drying"
}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.