Research & Design
Turntabl Labs explores emerging technologies, pre-builds client tech stacks, and validates AI capability through user research and product-market fit testing — so everything we ship is proven before it reaches production.
Case study: Basis - Safe, Realistic Data for Regulated Environments
The Context
Basis is an internal Turntabl initiative created to resolve the tension between data privacy and operational agility. It enables organisations in heavily regulated sectors—like the public sector and finance—to leverage production-like data for testing, QA, and analytics without the risk of exposing sensitive information.
The Core Challenge
Teams often face a "trilemma" where privacy, data utility, and speed conflict:
The Conflict: Production data is too sensitive to move, masking destroys meaningful patterns, and current synthetic data tools often fail to replicate real-world statistical behaviour
The Consequence: These blockers lead to slower delivery, reduced testing confidence, and high-risk manual workarounds
Our Approach: Learning, Not Just Generating
Basis was developed as a production-ready solution, shifting from a pure research project to an operational platform. Unlike traditional tools, Basis learns directly from real datasets to ensure high-fidelity outputs.
Architecture: Features lightweight agents for CI/CD pipelines, automated data generation, and a management console with role-based access
Integration: Designed to operate within client-owned infrastructure without requiring changes to core systems
Proven Outcomes
By bridging the gap between security and progress, Basis delivers:
High-Fidelity Utility: Preserves complex data relationships and real-world behaviour essential for modelling and QA
Regulatory Compliance: Eliminates exposure of sensitive personal data
Operational Impact: Faster delivery cycles, reduced manual data handling, and increased confidence in digital service validation