Building High-Fidelity Synthetic Tax Case Datasets for US Tax AI Evaluation
How Shaip created 2,000 realistic U.S. tax cases with federal and state returns, supporting documents, CPA-style workflows, financial domain expert validation, and de-identification controls for enterprise tax AI testing.
Project Overview: Synthetic Tax Dataset for AI Evaluation
As tax AI systems become more capable, the quality of evaluation data becomes a critical differentiator. The client required a large-scale dataset of realistic individual tax cases spanning federal filing requirements plus state-level variations across California, Texas, New York, Illinois, and Florida. Each case needed to replicate real CPA workflows and include a complete intake prompt, supporting source documentation, reconciled tax forms, executive summaries, and difficulty labels.
Shaip was engaged to design a production-ready dataset pipeline capable of generating 2,000 fully reconciled tax cases, with the operational capacity to scale to 5,000 cases as program needs expanded. The dataset needed to cover recent tax years, reflect balanced complexity across medium and moderately complex cases, & maintain strict quality controls around consistency, de-identification, formatting, and non-duplication.
Key Dataset Metrics
Dataset Volume
2,000 tax cases
Scalability
Capacity to expand to 5,000 cases
Supporting Documents
7-15 documents per case
Delivery Throughput
300-400 datasets per week
Tax AI Evaluation Challenges
- Ensuring every case mirrored a realistic CPA-style workflow with internally consistent taxpayer narratives, source documents, and final returns.
- Handling state-specific tax logic across five states while preserving alignment with federal forms and summaries.
- Producing medium and moderately complex tax scenarios involving HSA, multi-state income, K-1s, ACA forms, Schedule C, capital gains, and foreign accounts.
- Maintaining strict data integrity, formatting, and completeness standards, including PDF-only delivery and rejection of incomplete, duplicated, or structurally inconsistent files.
- Protecting privacy through synthetic or anonymized datasets with de-identification safeguards and multi-stage review.
Shaip’s Synthetic Tax Data Solution
Data Strategy
Shaip structured the engagement around the production of 2,000 realistic individual tax cases, each designed for evaluation & testing use. The workflow was built to support future scale-up to 5,000 cases without compromising consistency or quality. Cases were designed to represent the last five tax years, with stronger representation from recent filing periods.
Case Design & Intake Modeling
Each case included a detailed client intake questionnaire covering personal details, filing status, dependents, employment, stock compensation, retirement income, 1099 income, K-1s, rental income, foreign income, deductions, credits, compliance history, and state-specific requirements. This ensured each scenario reflected the information-gathering stage of a real tax engagement.
Document Package Creation
To make each file set lifelike and evaluation-ready, every case included a package of supporting records such as W-2s, 1099-INT/DIV/B, 1099-R, 1099-NEC/MISC, K-1s, 1095-A, mortgage interest forms, property tax bills, brokerage statements, rental agreements, bank statements, business expense receipts, and HSA/IRA records.
Return Preparation & Reconciliation
Each dataset included completed federal & state tax return forms, including Form 1040 and applicable schedules, plus state filing forms & related credit or voucher documents where required. Short-form executive summaries captured AGI, taxable income, total tax, payments, refund or balance due, penalties, and effective tax rate, with state-level summary fields.
Complexity Framework
Cases were organized in defined difficulty levels, with emphasis on Level 2 (Medium) & Level 3 (Moderately Complex) scenarios. These included multi-state filing situations, HSA activity, Schedule C income, capital gains, ACA reporting, foreign accounts, and K-1 driven tax logic.
Quality Assurance & Acceptance Controls
Shaip aligned delivery to strict quality requirements covering logical consistency, tax field mapping, document completeness, structural conformity to U.S. tax templates, and final audit readiness. The workflow also accounted for rejection criteria around incorrect PDFs, missing financial details, duplicated datasets, and mismatched field placement.
Privacy & Compliance
All data was designed to be synthetic or properly anonymized, with no real PII and multi- stage de-identification review. This ensured the dataset could support enterprise testing needs while maintaining privacy discipline.
Synthetic Tax Dataset Scope
| Dataset Component | Scope |
|---|---|
| Case Volume | 2,000 cases |
| Scalability | Expandable to 5,000 cases |
| Geography | California, Texas, New York, Illinois, Florida |
| Documents Per Case | 7–15 documents |
| Difficulty Levels | Level 2 and Level 3 focus |
| Federal Forms | 1040, Schedules 1–3, A, B, C, D, E, SE, and applicable forms |
| State Forms | Relevant state return forms, schedules, credits, and vouchers |
| Summary Output | Federal + state tax summaries |
| Delivery Format | PDF-only |
| Weekly Throughput | 300–400 datasets |
Outcome: Enterprise-Ready Tax AI Evaluation Dataset
- Created a framework for 2,000 high-fidelity tax cases designed for internal evaluation and testing
- Established operational readiness to scale production to 5,000 cases
- Enabled realistic model testing across federal + five-state tax workflows
- Structured each case to reflect true CPA-style intake, documentation, filing, & summary logic
- Built in strict controls for de-identification, reconciliation, completeness, and PDF formatting
Overall, this engagement demonstrates how Shaip can help tax AI teams move beyond generic examples and into enterprise-grade evaluation datasets that reflect real filing complexity, multi-document reasoning, and jurisdiction-specific tax behavior. The result is a stronger foundation for model benchmarking, QA, and internal product validation.
Shaip brought structure, rigor, and scalability to a highly nuanced tax data initiative. Their ability to translate complex federal and state filing requirements into realistic, reconciled case datasets created a strong foundation for our AI evaluation workflows.
– Head of Tax AI Solutions
