Intelligence should be useful. It should not pretend certainty.

1. 01

   Evidence before conclusions

   A system should show what the available information supports before it offers an interpretation. Conclusions without a visible basis are not useful in high-stakes work.

2. 02

   Human judgment remains important

   Dianalab products are built to help people see, compare, and decide. They are not built to quietly replace the person accountable for the decision.

3. 03

   Uncertainty should be visible

   When a model is guessing, incomplete, or working from thin evidence, that should be obvious. Hidden confidence is more dangerous than an honest gap.

4. 04

   Privacy by design

   Products that handle video, interviews, and personal documents should collect only what the workflow requires and keep that information within the intended use.

5. 05

   Security by design

   Access, storage, and sharing should be treated as part of the product, not as a later overlay. We do not publish internal security architecture on this site.

6. 06

   Systems should be testable

   If a behavior cannot be inspected, reproduced, or evaluated, it does not belong in a product people rely on.

7. 07

   Models should not invent evidence

   Generated language is not a substitute for source material. If the footage, transcript, or document does not contain something, the product should not present it as found.

8. 08

   AI output should be distinguishable from source evidence

   People should be able to tell the difference between original material and a model’s summary, suggestion, or reconstruction.

9. 09

   High-impact decisions require appropriate human review

   Investigation findings, interview assessments, and similar outputs are inputs to human work. They are not automatic determinations.

This page does not claim certification, audit results, or compliance with any named regulation.