AI-generated, realistic test data for any Salesforce object — schema-aware, dependency-ordered, and validated before every insert. Empty sandboxes make for bad testing.
Hand-keying records is slow, CSV templates go stale, and "Test Account 47" doesn't exercise anything. AI generation against your live schema fixes all three.
The tool reads your object and field metadata — types, lengths, picklists, required fields — and generates realistic records that fit. Names look like names, industries match your picklists, dates make sense.
Parents before children, always: Account → Contact → Case, resolved by topological sort. Child records link to parents created in the same run — no orphans, no manual ID bookkeeping.
A dynamic object picker offers every createable object in your org — standard or custom. Choose the objects and volumes; the generator does the rest.
Every record is checked before it's written: oversized strings are truncated, invalid fields removed. Failures don't cascade — if a parent can't be created, its children are skipped instead of erroring out.
Share, History, and Feed objects — and auto-created junctions — are filtered out automatically, so generated data only lands where data belongs.
Watch records being created in real time, object by object, with a full account of what was generated when the run completes.
Developer and scratch orgs start empty. Generate a realistic dataset in minutes so automation, validation rules, and flows get exercised against data that behaves like production's.
Client demos and admin training land better with believable Accounts, Contacts, and Cases than with rows of "Test 1". Generate a clean, realistic dataset per session.
Before running Data Migration against production, rehearse against a sandbox seeded with representative volumes — parent-child structures included — to validate mappings and timing.
The tool reads your org's object and field metadata first — types, lengths, picklist values, required fields, and relationships — then generates data that fits that schema. Records look like your business's records, not lorem ipsum.
Dependencies are resolved automatically through topological sorting: parents are created before children (Account before Contact before Case), and child records are linked to the parents created in the same run. If a parent fails to create, its children are skipped rather than inserted as orphans.
No. Smart object filtering automatically skips Share, History, and Feed objects along with auto-created junction objects, and the object picker only offers createable objects. Pre-insert validation also truncates oversized strings and removes invalid fields before anything is written.
It writes only to the org you select, and the typical target is a sandbox or scratch org. It creates new records — it never modifies or deletes existing data — and live progress streaming shows exactly what was created in each run.
Generated data is the safest way to rehearse the real thing.
Rehearse your migration against a seeded sandbox, then run it for real — schema comparison, relationship-aware ordering, and live monitoring included.
Learn more →Auditing what's already in an org? Count files and attachments across objects before deciding what to generate, migrate, or archive.
Learn more →Tell us about your org and what you're testing. We'll generate a representative dataset live so you can see the quality for yourself.