Syntharil generates schema-valid synthetic data for standard and custom objects: realistic names, emails, and addresses, valid picklist values, and parent-to-child relationships built automatically. Never “Test Account 1” again.
Empty sandboxes make everything slower: demos fall flat, QA misses bugs that only appear with real-shaped data, and every developer wastes time hand-crafting records. Generated data solves it only if it respects your schema — required fields, picklists, lookups, record types — and looks like something a real business produced.
Authorize the org, choose the objects to seed, and set how many records of each you want.
Pick a generation method, override individual fields (skip, fixed value, pattern, range, or AI), and set the geographic mix.
Records are created parent-first with valid lookups, respecting field types, lengths, required flags, and picklist values — with live progress as they land.
Field types, lengths, required flags, and picklist values come from your org’s live schema — including custom objects and fields.
Parents are created first and children reference their real IDs, so Account → Contact → Opportunity chains work out of the box.
Names, emails, phone numbers, and locale-aware addresses that read like real records — not random strings.
Any field can be skipped, fixed, pattern-generated, range-bounded, or handed to the AI generator.
Control the US-versus-international mix so the data matches where your business actually operates.
Optionally truncate previously seeded data before a run, so repeated seeding stays predictable.
Standard generation uses Faker-style synthesis: fast, schema-valid, realistic-looking values for every field. Persona-anchored generation goes further — it builds coherent personas from industry preset libraries and keeps them consistent across objects, so a contact’s name, email domain, employer, and address all tell the same story. An optional deterministic seed makes runs reproducible.
For fields that need judgment — descriptions, notes, industry-specific text — AI-powered field generation writes values that fit the record’s context. The live AI Playground shows exactly what a field configuration will produce before you run anything.
Comparing your options? Read 9 ways to generate realistic Salesforce test data → — or copy real records instead with replication →. Comparing tools? See test data tools compared →.
For repeatable, realistic data with valid relationships, use a schema-aware generator: it reads your org’s field types, picklists, and lookups and builds records that pass validation, parents before children.
Yes. Generation is driven by your org’s live schema, so custom objects, custom fields, and their picklists and required flags are all respected.
Objects are seeded in dependency order — parents first — and child records reference the real IDs that were just created.
Yes. Individual fields can be routed to AI generation for context-aware text, and the AI Playground previews the output before a run.
Yes. A run can truncate previously seeded records first, so you always start from a known state.
Use replication: it copies real records into a sandbox you own with PII masked in transit — synthetic and replicated data cover different testing needs.