High-Fidelity Synthetic Data
That Looks And Behaves Like Yours
We know one-size-fits-all data doesn’t work. Our synthetic data mimics the shape, logic, and messiness of your world - safely.
How It Works
We don’t need your real data - just the structure that defines it. From there, we create high-fidelity synthetic datasets that look, feel,
and behave like your own, ready for safe testing inside your NayaOne sandbox.
Share your data schema
We don’t need your real data — only your table structures and key fields.
Define what matters
Tell us the test cases, data behaviours, and statistics you want represented.
We configure agent-based generators
Our agents create data row by row, following your logic.
Generate and validate
You receive a dataset that mirrors your world, ready to use in a secure sandbox.
Your Data Stays Yours
We never touch production data. You keep full control - we simply replicate its structure so you can run proofs safely.
Your Schema
Share table structures, keys, and relationships — no real data required.
NayaOne Generator
We configure agent-based generators that follow your logic row by row.
Secure Sandbox
Receive a sandbox-ready dataset that mirrors your world for safe validation.
One Platform.
Multiple Synthetic Data Use Cases.
RISK AND COMPLIANCE
Identity and Onboarding
Generate synthetic identity documents and onboarding records to safely test KYC/KYB and customer verification processes - without using real customer information.
- Identity Document Sets
- Customer Onboarding Forms
- KYB Company Records
FRAUD AND FINANCIAL CRIME
Fraud and Risk
Generate datasets that include edge cases, anomalies, and suspicious patterns so risk teams can test fraud detection models and resilience - without exposing live customer data.
- Fraudulent transaction records
- Anomalous Account Activity
- Suspicious Case Logs
RISK AND COMPLIANCE
Regulatory Compliance
Generate datasets that replicate reporting obligations and audit scenarios, helping compliance teams validate systems against regulatory standards without exposing sensitive data.
- Transaction Monitoring Records
- Audit Trail Datasets
- Regulatory Reporting Files
CREDIT AND LENDING
Lending and Credit
Create loan application, repayment, and credit history data that mirrors production environments, enabling fair testing of credit decisioning tools and lending platforms.
- Loan Application Data
- Repayment History Tables
- Credit Score Reports
INSURANCE
Claims and Insurance
Produce synthetic claims, policy, and payout datasets so insurers can test AI, automation, and fraud detection in claims processes — all without customer exposure.
- Insurance Claims Records
- Policyholder Data Tables
- Payout History Files
AI AND AUTOMATION
AI and Model Testing
Provide high-volume, production-like data for testing AI/ML models, ensuring governance and compliance without relying on customer datasets.
- Tabular Datasets
- Synthetic Text Corpus
- Balanced Demographic Data
Types of Data We Generate
High-fidelity datasets that stay consistent across systems, built to replicate the complexity of your production environment.
Structured
Claims, policies, and transaction records that mirror production systems.
Semi-Structured
Create event logs, payment flows, and activity streams to test integrations and workflows.
Unstructured
Contracts and ID documents in realistic formats.
Clean and Messy
Perfect datasets or noisy ones to test resilience.
What Our Customers Say
Huge thanks to our partner NayaOne, and I look forward to seeing the outcomes of the benchmarking tech spring later this week in London.
This dataset includes 2000 Equity products, comes with Settlement Logic, Business Calendar Alignment, Option Strikes and is also compliant with CFI and EMIR Codes, ISDA Taxonomies, LEI Identifiers and UTI Generation.
This data is designed to help you build, test, and benchmark next-gen trade reporting agents and beyond.
From locked data to live evidence - in weeks, not months.
See how high-fidelity synthetic data unlocks faster proof-of-concepts and enterprise-grade validation.
FAQs
We start with your data schema, not your real data. You share the structure – tables, relationships, and field types – plus any behaviours or test cases you want represented.
From there, our agent-based generators build data row by row, mirroring the logic, complexity, and quirks of your environment.
The result: synthetic data that behaves like the real thing, without ever exposing live information.
Yes. NayaOne supports both structured datasets (tables, transactions, logs) and unstructured data (text, documents, images). We can simulate structured relationships – like customers, accounts, and transactions – and also create realistic document data for areas such as claims, KYC, or support conversations. If your use case spans multiple data types, the generators can connect them all in one coherent dataset.
Absolutely. Every dataset maintains referential integrity, meaning linked tables and entities (like customer → account → transaction) stay consistent. This ensures synthetic data behaves the same way your systems expect it to – crucial for running accurate AI training, API validation, and integration testing.
Typically a few days to a few weeks, depending on complexity. Once we receive your schema and test requirements, we configure the generators and return a first version for review. Unlike traditional anonymisation or manual data prep, this process is fast, repeatable, and can be reused across multiple PoCs or projects.
All synthetic data stays within your secure NayaOne sandbox environment. We never move or access your production systems. The data generation, validation, and testing all happen inside isolated, governed workspaces that align with your security and compliance requirements.
Yes. Once your generators are configured, you can tweak variables (volume, frequency, specific entities, edge cases) and instantly produce new datasets. This is ideal for regression testing, new AI models, or when your schema evolves.
