Quick Sigorta
Realistic load testing, zero data leaks
Overview
Quick Sigorta is an insurance company operating with more than thirty million policies and over eight thousand agencies. Because its data falls under regulation, producing realistic test data was a persistent constraint. In the work we carried out with RabbitQA and Loadmance, load testing was run without production data and no data leak occurred at any point in the process.
- Company
- Quick Sigorta
- Scale
- 30M+ policies · 8,000+ agencies · 150+ employees
- Industry
- Insurance
- Products used
- RabbitQA · Loadmance
- Award won together
- The AI Awards 2026, Best Ethical Risk Management in AI Output
Before and after
| Measure | Before | After |
|---|---|---|
| Data used in load testing | Dependent on production data | Synthetic data, statistically consistent |
| Sensitive data exposure | Present in the test environment | No production data used at any stage |
| Auditability of the test process | Undocumented | Recorded end to end and audit ready |
| Control over AI output | No defined layer | Output control layer, decision stays with the team |
Industry need
In insurance, test data is a compliance question rather than a technical one
In insurance organisations the essential difference between the test environment and production is not data volume but the legal status of the data. Policy and claim records contain personal data, and copying those records into a test environment is not a defensible practice under regulation.
This constraint is commonly worked around in one of two ways. The first is to use masked production data, which reduces realism because masking breaks referential integrity. The second is to work with small, manually assembled data sets, which leaves load behaviour and edge cases invisible. In both cases the test result no longer represents production behaviour.
Quick Sigorta needed to remove that trade-off between realism and compliance: a load testing model that never touches production data yet still represents how production behaves.
Product
We put synthetic data generation at the foundation of the load test
Deriving the data requirement
We defined the data variety the test scenarios required by working through the policy and claim flows. We determined which field had to be generated at which distribution so that the set would represent production behaviour.
Project outcome
Compliance and realism were achieved in the same engagement
No production data was transferred into the test environment at any stage of the load testing process. No data leak occurred and sensitive data exposure was kept outside the process entirely.
Because the test data represents production behaviour statistically, the load test results became usable for decision making. Edge cases were brought into scope in a way that manually assembled data sets would not have revealed.
Since the process is fully documented, the testing work can answer an audit request immediately. How each data set was generated and what result it produced is on record.
The work received the Best Ethical Risk Management in AI Output award at The AI Awards 2026.
Products used in this project
Multi-agentic AI platform
RabbitQA
In this project RabbitQA provided synthetic test data generation and the AI output control layer. Every record generated and every test result was made traceable, and the acceptance decision stayed with the team.
Explore the RabbitQA platform
Cloud-Based, AI-Powered Performance Testing
Loadmance
In this project Loadmance tested policy issuance and claim flows under realistic load. System behaviour was measured across peak scenarios and reported flow by flow.
Explore Loadmance