Questions and Answers

Why would I need DAIXIS?

Because most scientific risk doesn’t come from bad data.
It comes from how conclusions are formed, framed, and repeated over time.

In regulated environments, interpretations often:

• harden too early,
  
  

• drift as they move between teams,
  
  

• lose their original assumptions,
  
  

• and become treated as facts they were never meant to be.
  
  

DAIXIS exists to discipline that moment, the point where evidence becomes judgment, before it quietly turns into organizational truth.

How does DAIXIS actually help?

DAIXIS helps by doing something most systems don’t:

It makes interpretation explicit, bounded, and defensible.

Specifically, DAIXIS helps teams:

• distinguish what was observed from what was inferred,
  
  

• document what the evidence supports and does not support,
  
  

• surface reasonable alternative interpretations,
  
  

• preserve uncertainty instead of erasing it,
  
  

• and clearly assign ownership for interpretive judgments.
  
  

This doesn’t slow science down.

It prevents over-confidence from outrunning evidence.

Will DAIXIS tell me what decision to make?

No.

DAIXIS does not recommend actions, predict outcomes, or replace expert judgment.
All decisions remain fully owned by the organization.

DAIXIS focuses on how conclusions are justified, not which conclusions must be chosen.

Does funding, investor pressure, or commercial interest influence DAIXIS outputs?

No.

DAIXIS is designed to be funding-agnostic by design. Its governance rules, interpretation boundaries, and evidentiary constraints do not change based on funding source, contracts, investor expectations, or organizational incentives.

If an interpretation cannot be supported within defined constraints, DAIXIS will not permit it — regardless of external pressure. This invariance is intentional and foundational to the system.

Will DAIXIS save me?

DAIXIS is not a rescue system, and it’s important to be clear about that.

DAIXIS won’t:

• prevent every bad outcome,
  
  

• eliminate scientific uncertainty,
  
  

• or guarantee regulatory approval.

What it can do is far more realistic and far more valuable:

DAIXIS helps ensure that when decisions are questioned later — by leadership, auditors, regulators, or legal teams — you can clearly show:

• what was known at the time,
  
  

• how it was interpreted,
  
  

• what limits were recognized,
  
  

• and who owned the judgment.
  
  

That is not certainty.

It is defensibility.

Is DAIXIS for teams that don’t trust their scientists?

No.

DAIXIS is for teams that trust their scientists, but understand that:

• memory fades,
  
  

• language shifts,
  
  

• and organizations are larger than any one expert.
  
  

DAIXIS protects good judgment from being flattened, simplified, or misused downstream.

Isn’t this something a smart leader already does mentally?

Often, yes.

But undocumented reasoning:

• can’t be audited,
  
  

• can’t be transferred,
  
  

• can’t be defended later,
  
  

• and doesn’t scale beyond the individual.
  
  

DAIXIS doesn’t replace thinking.

It turns thinking into an organizational artifact.

Does DAIXIS depend on a specific AI model or technology?

No.

DAIXIS is not defined by any specific AI model. Where inference engines are used, they operate as replaceable, subordinate components within DAIXIS governance constraints.

Inference engines do not determine meaning, weigh evidence, or resolve ambiguity. DAIXIS governs how interpretation is structured, bounded, and documented independently of the underlying technology.

This ensures that interpretive discipline remains consistent even as tools change.

Who is DAIXIS really for?

DAIXIS is most valuable for teams operating where:

• scientific conclusions have real consequences,
  
  

• interpretation choices carry regulatory or legal weight,
  
  

• and “what the data meant” may be questioned long after the decision was made.
  
  

That typically includes Medical Affairs, Safety, Compliance-adjacent functions, and scientific leadership.

How do I know if DAIXIS is relevant to me?

If you’ve ever said:

• “That’s not what the data actually showed,”
  
  

• “That’s not what we meant originally,” or
  
  

• “That conclusion drifted over time,”
  
  

then DAIXIS is relevant.

How can I learn more about DAIXIS or work with DumstorfAI?

Because interpretation governance depends heavily on organizational context, detailed implementation discussions happen directly with clients.

For inquiries, collaborations, or pilot discussions, contact

info@dumstorf.ai

or visit www.dumstorf.ai