Generate from schema,
not from production data
Define columns, types, and constraints. Describe your domain. Generate hundreds of realistic rows - customer profiles, demographics, objection patterns - with balanced distributions and zero PII. No seed data required.
| # | customer_name text | age integer · 18–85 | policy_type text · categorical | risk_level text · categorical | objection text | sentiment text · categorical | premium float · 50–500 |
|---|---|---|---|---|---|---|---|
| 1 | Maria Gonzalez | 34 | Auto | Medium | Premium is too high for basic coverage | Frustrated | $187.50 |
| 2 | James Chen | 72 | Health | High | Need to understand Medicare supplement | Confused | $412.00 |
| 3 | Priya Sharma | 28 | Renters | Low | Do I really need this? | Skeptical | $62.00 |
| 4 | Robert Williams | 45 | Auto | Medium | Switching from competitor, want better rate | Neutral | $203.75 |
| 5 | Aisha Johnson | 55 | Home | High | Claim was denied, very upset | Angry | $341.25 |
| 6 | Tomasz Kowalski | 39 | Health | Low | Want to add dependent coverage | Positive | $278.50 |
| 7 | Sarah Mitchell | 61 | Auto | Medium | Accident last month, worried about rates | Anxious | $295.00 |
Schema in, realistic data out -
no seed data required
Define columns with names, types (text, integer, float, boolean, array, JSON, datetime), and constraints (min/max, categorical values). Add detailed descriptions for each column - the richer the description, the more realistic the output. No seed data required - generate from scratch, not by anonymizing production data.
Create your first datasetOptionally connect a knowledge base so generated data reflects your actual domain - product catalogs, policy documents, FAQs. Every row stays factually grounded in your source material. Add use-case descriptions and pattern instructions to guide tone, style, and content distribution.
Connect a knowledge baseCategorical columns respect the values you define. Numeric columns stay within min/max constraints. Cross-column relationships are maintained - positive sentiment correlates with higher ratings, not random values. The result is data that looks like real production traffic, not a random number generator.
See quality guaranteesUnlike tools that anonymize real data with differential privacy, synthetic data here is generated from scratch - PII was never present. No real customer names, addresses, or identifiers. No toxicity. No copyright material. Safe for sharing across teams, compliant with GDPR, CCPA, and HIPAA by default.
Learn about safety guardrails Test data that matches
your real-world traffic
Generate customer profiles for simulations
Define demographics, objection patterns, risk profiles, and insurance types as columns. Generate hundreds of realistic customer personas that drive voice and chat simulations - no real customer data needed.
Bootstrap evaluation datasets from zero
No production data yet? Define your schema, describe the domain, and generate a complete evaluation dataset in minutes. Go from zero test cases to hundreds - without waiting for real traffic.
Cover edge cases and adversarial inputs
Generate the tricky inputs your team would never think of - misspellings, ambiguous requests, hostile tones, multi-language queries. Describe the edge cases you want and the system creates realistic rows that stress-test your agent.
Test in regulated industries without PII
Healthcare, finance, and insurance teams can generate realistic test data that never contains real patient records, account numbers, or personal identifiers. Compliant with GDPR, CCPA, and HIPAA by default - because PII was never present.
Scale from 50 to 5,000 test cases
Add rows to any existing dataset with a prompt or upload. Fill coverage gaps identified by simulation reports. Generate 1–100 rows per request, or bulk-generate from a CSV/Excel schema.
Feed directly into experiments and scenarios
Synthetic datasets plug directly into experiments for prompt-model comparison and into scenarios for simulation testing. No export/import step - the data is already in the platform, ready to use.
From schema to
dataset in minutes
Define schema and describe your domain
Add columns with types and constraints. Write detailed descriptions for each column - demographics, objection patterns, risk profiles, or whatever your domain needs. Optionally connect a knowledge base for grounded generation.
Set row count and generate
Choose how many rows to generate (minimum 10, no practical limit). Add use-case context and pattern instructions to guide tone and style. Click generate - the system creates realistic, balanced data that respects your schema.
Use in scenarios, experiments, or export
The dataset is immediately available in the platform. Use it to drive simulation scenarios, run prompt-model experiments, or export as CSV, JSON, or Excel. Add more rows anytime - from AI generation, file upload, or manual entry.
Powering teams from
prototype to production
From ambitious startups to global enterprises, teams trust Future AGI to ship AI agents confidently.