Trust is an architecture.
Not a policy.
We built a hard wall between the models that write your code and the environment that holds your data.
No clinical data ever touches AI.
The #1 concern when introducing automation to evidence generation is whether the output can be explained and defended. You cannot build evidence on a black box.
That is why Frekil's engine only writes the statistical logic (Python/R/SQL). The generated code is entirely transparent. You review it, you approve it, and then it is executed in a completely isolated, sandboxed environment where your structured clinical data lives.
Absolute Separation
Our AI models generate analytical logic. They never read, process, or train on your clinical patient data. The separation is an architectural boundary, not a configuration option.
Transparent Code
Frekil doesn't produce black-box outputs. It writes standard, reviewable Python, R, and SQL. If anyone asks how a number was derived, the exact script is right there.
Immutable Audit Trail
Every transformation, from raw EHR extraction to the final Kaplan-Meier curve, is logged. The exact code version and data snapshot are pinned permanently.
Deterministic Execution
Run the same analysis a month later, get a byte-identical result. Our sandboxed execution environment ensures your evidence is strictly reproducible.