Synthetic Onboarding — Employee Personal Information Data

What this document is

The employee personal information sheet is the first page of almost every new-hire packet: full legal name, preferred name, job title, home address, email, mobile and home phone, date of birth, Social Security number and marital status, plus employer-completed boxes for department, employee ID and start date. Everything the HRIS knows about a person starts here, which means every error here propagates into payroll, benefits and tax filing.

Why generate synthetically

Onboarding paperwork is the single largest category of document an HR-tech platform ingests and the single hardest to obtain for training. A real packet is a named individual's identity documents; the employers who hold them cannot release them, and the vendors who process them cannot retain them. So new-hire capture is typically built against a handful of internal samples and shipped on faith. Synthetic packets give the same document in volume, with ground truth, and with nothing to protect.

What makes synthetic data useful

Each generated sheet is one simulated new hire whose details stay consistent across their whole onboarding packet — the same legal name, address, date of birth and Social Security number appear on their I-9, their W-4, their direct deposit authorisation and their beneficiary designation. That coherence is what makes the corpus useful beyond single-form extraction: it supports the cross-document identity matching that packet-processing software actually has to perform. Preferred name differs from legal name the way it does in real life, and marital status splits 47.1% married, 45.9% single and 7% other.

Training challenges

Two adjacent name fields — full legal name and preferred name — carry similar-looking values and are trivially swapped, and a swap here renames the employee in every downstream system. Three fields on the form are completed by the employer rather than the new hire and render empty on every generated document, so a model that fills department, employee ID or start date from nearby text is hallucinating rather than reading. Home phone appears on only 19.8% of sheets while mobile is always present, which makes phone-field assignment a real decision instead of a positional lookup.

Who uses this data

HR-tech onboarding suites automating new-hire packet capture, HRIS and payroll integrators building document import, background-check providers parsing submitted identity forms, and employer-of-record platforms that ingest packets from many different employers' templates.

Document complexity profile

18 fields on a single page: 15 text and 3 checkbox targets forming one mutually exclusive marital-status group, with one label-to-value relation per field. Three of the text fields are employer-completed and empty on every document. 2 arithmetic bindings and 8 function calls derive the demographic values from the simulated identity, at maximum expression depth 1.

Key stats from our synthetic corpus

Quantitative characteristics of the Onboarding — Employee Personal Information documents our generator produces.

How this document co-occurs with others

Rates at which identities in our corpus that produce a Onboarding — Employee Personal Information also produce other documents.

Prevalence rates above are corpus-derived: they were computed over the 641 employment-eligible identities inside a local synthetic corpus of 1,000 identities generated by SymageDocs' World Simulation Engine at seed 20260421 — the shipped definition gates onboarding forms on active employment. Field, type and relation counts and the empty employer-completed fields come from the shipped employee personal information definition in the SymageDocs form library. No real employee data was used at any stage.

Frequently asked questions

What data format do synthetic employee information forms include?

Each generated identity produces a rendered PDF plus a structured JSON annotation file with bounding boxes, field types, and ground-truth values for all 18 fields — 15 text and 3 checkbox targets — with one label-to-value relation per field. COCO, YOLO, FUNSD, and BIO/NER exports come from the same job.

Do the details match the rest of the onboarding packet?

Yes. Every form generated for the same simulated identity carries the same legal name, address, date of birth and Social Security number, so a packet of eight onboarding documents describes one coherent person. That is what lets you evaluate cross-document matching rather than only per-document extraction.

Why are department, employee ID and start date always blank?

Because they are employer-completed boxes, filled in after the new hire returns the form. They are annotated and empty on every document, which makes them supervised negatives — the fields most likely to be populated with a hallucinated value by a model that expects every labelled box to have content.

Is the Social Security number real?

No. Every Social Security number in the corpus is generated for an identity that does not exist. The numbers are correctly formatted so that format validation and redaction logic can be exercised, and they belong to nobody.

Can I use this data commercially?

Yes. Every name, address, identifier and date is synthetic, contains no real personal data, and is licensed for commercial use including model training, benchmarking, and redistribution inside your own products.