One platform, many ways to use fake data
From CI pipelines to sales demos to ML training sets, Example Company replaces the messy, risky workflows teams use today to source non-production data.
Solutions by use case
The most common patterns we see across our customer base.
Software testing & QA
Generate deterministic test fixtures from a 64-bit seed. Drop-in replacement for hand-maintained factory_bot / Faker scripts. Supports JUnit, Jest, pytest, RSpec, and Go testing.
Customer example: Initech's payment platform team cut average test suite runtime from 18 minutes to 6 by switching from production DB snapshots to Example fixtures.
Sales demos & sandboxes
Spin up branded demo environments populated with industry-appropriate data. Each prospect gets their own sandbox seeded with their company name, logo, and pricing currency.
Customer example: Fabrikam Analytics provisions 200+ demo tenants per week from a single Example template, with zero cross-contamination of real customer data.
Machine learning & AI
Augment training sets, balance rare classes, and create labeled data for edge cases your production logs never captured. Our differential-privacy mode lets you boost real datasets without leakage.
Customer example: Umbrella Biotech trained a fraud-detection model on 8M synthetic claims plus 200k real labels, beating their previous benchmark by 14 F1 points.
Developer onboarding
New engineers clone the repo, run example seed, and have a realistic local database in 90 seconds. No VPN, no production access requests, no waiting on IT.
Customer example: Wayne Enterprises reduced new-hire ramp time from 2 weeks to 3 days after standardizing on Example for local dev environments.
Data sharing with partners
Share structurally identical datasets with vendors, integration partners, and contractors. They get something they can actually build against; you keep your customer data in-house.
Customer example: ACME Corp's API team ships an Example-generated reference dataset alongside every SDK release so integration partners can build offline.
Load & performance testing
Need to simulate 50 million users hitting your API on Black Friday? Stream synthetic traffic directly into k6, JMeter, Gatling, or Locust. Generate write workloads at sustained 1M ops/sec.
Customer example: Contoso Retail caught a database index regression two weeks before launch by load-testing with 120M synthetic orders.
Solutions by industry
We ship pre-built schemas tuned to the formats, regulations, and edge cases of each vertical.
Healthcare
HL7, FHIR R4, ICD-10, CPT, NDC, lab panels, imaging metadata, claims. Designed for regulated healthcare deployments — no real patient data ever in our pipelines.
Financial services
Card transactions, ACH, wire transfers, KYC documents, market data ticks, options chains, loan applications. SWIFT MT and ISO 20022 message formats supported.
Retail & e-commerce
Product catalogs, SKU hierarchies, carts, orders, returns, loyalty programs. Multi-currency, multi-locale, multi-tax-jurisdiction out of the box.
Insurance
Policies, claims, underwriting records, actuarial tables. Pre-built generators for auto, home, life, and commercial P&C lines.
Logistics & supply chain
Shipments, waybills, customs declarations, vehicle telemetry, warehouse inventory. Built-in geospatial realism using OpenStreetMap road networks.
Telecom
CDRs, IPDRs, subscriber records, billing events, network topology. Generate at carrier scale — billions of events per day on Enterprise.
SaaS & B2B
Multi-tenant accounts, users, roles, audit logs, billing events, feature flags. Realistic usage curves including churn, expansion, and seasonality.
Government & public sector
Census-like demographics, voter rolls, permit applications, transit data. Government-authorized deployment options available on the Enterprise tier.
Media & ad tech
Impressions, clicks, conversions, bid streams, viewability events. Generate ad-exchange-grade volumes with realistic auction dynamics.
Integrations
Example Company plugs into the tools your team already uses.
Databases
Postgres, MySQL, MariaDB, SQL Server, Oracle, MongoDB, DynamoDB, Cassandra, Redis
Warehouses
Snowflake, BigQuery, Redshift, Databricks, Synapse, ClickHouse, DuckDB
Streaming
Kafka, Kinesis, Pub/Sub, RabbitMQ, NATS, Pulsar
Object storage
S3, GCS, Azure Blob, R2, MinIO. Parquet, Avro, JSONL, CSV, ORC.
Orchestration
Airflow, Dagster, Prefect, Argo Workflows, Step Functions
Transformation
dbt, SQLMesh, Coalesce
CI / CD
GitHub Actions, GitLab CI, CircleCI, Buildkite, Jenkins
Observability
Datadog, New Relic, Honeycomb, Grafana Cloud
Platform capabilities
Everything you get on day one.
| Capability | What it does |
|---|---|
| Schema inference | Reads your existing schema and infers types, constraints, and foreign-key relationships automatically. |
| Referential integrity | Multi-table generation preserves FK consistency even across millions of rows and dozens of joins. |
| Custom generators | Write Python or TypeScript functions for column-level generation. Mix faker primitives, regexes, and LLM-backed prose. |
| Differential privacy | Train generative models on real data with epsilon/delta bounds you control. Ship the synthetic output with audit-ready provenance. |
| Deterministic seeds | The same seed + config always produces the same dataset, byte-for-byte, across versions and machines. |
| Streaming output | Pipe directly into Kafka or S3 multipart uploads without materializing to disk. |
| Schema versioning | Track config in git. Diff datasets across versions. Roll back when generators regress. |
| RBAC & SSO | SAML, OIDC, SCIM. Per-project roles. Available on Pro and Enterprise. |
| VPC deployment | Self-hosted control plane in your AWS, GCP, or Azure account. Enterprise tier. |
| Audit log export | Every generate, download, and config change streamed to your SIEM. Enterprise tier. |
Not sure which solution fits?
Our solutions team will walk you through a proof of concept in your environment, free.
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