Open beta — every feature free with your own AI API key. Beta software: expect (and report!) rough edges.

Data Seeding

Salesforce sandbox seeding with built-in data masking

Select objects and filters from a source org, apply field-level masking rules to protect PII, and load realistic test data into a sandbox — in the right order, with relationships intact, as a resumable background job.

What you can do

  • Pick objects with SOQL filters — seed exactly the slice of data you need
  • Automatic PII detection flags emails, phones, addresses, and identity fields
  • Masking strategies per field: hash, redact, scramble, replace, randomise in range, shift dates
  • Relationship-aware loading: parents before children, with old→new ID remapping
  • Dry runs to test a plan end-to-end without loading anything
  • Reusable seeding plans and resumable, cancellable background runs

How it works

1

Build a plan

Choose objects, filters, and masking rules — the PII detector suggests fields you shouldn’t copy raw.

2

Dry run

Extract and transform without loading, and check the counts and masking manifest.

3

Seed the sandbox

Detached background job with progress you can watch, leave, and resume. The target must be a sandbox — production is refused.

Why it's different

Masking is mandatory, not a checkbox

Field-level transforms are applied before data leaves the source org, the run stores a manifest of which strategy hit which field (never the values), and the loader refuses production targets outright. Compliance by construction.

Try asking

  • Give every developer sandbox realistic accounts, opportunities, and cases
  • Refresh UAT with current production-shaped data — minus the real PII
  • Reproduce a data-dependent bug safely
  • Stand up demo environments with masked but realistic data

Data Seeding — questions

Put Data Seeding to work on your org

Start free with the Metadata Explorer — every AI feature is free while the beta runs, and a competitive price is still being worked out.