AI is moving
from answers
to actions.
Capability doesn't equal authority.
AIM™ is the Trust + Authority Layer for AI. It uses evidence to dynamically determine what an AI system should be allowed to do next.
Member of the
NVIDIA Inception program
A MORE
TRUSTWORTHY
AI FUTURE.
AI can increasingly act.
Who decides
when it should?
AI systems are moving beyond generating information.
They can recommend.
Communicate.
Coordinate.
Decide.
Transact.
Execute.
Capability answers:
What can AI do?
AIM addresses another question:
What should AI
be allowed to do?
Capability
Can the system act?
- Generate content
- Communicate
- Coordinate
- Decide
- Transact
- Execute
AI can do more than ever before.
Authority
Does the available evidence justify allowing it?
- Verified signals
- No contradictions
- Within policy
- Sufficient context
- Real-time evaluation
AIM determines what should be allowed.
Trust becomes
authority.
AIM™ is the Adaptive Integrity Model developed by Veraxius.
It converts evidence into dynamic trust states and uses those states to inform bounded authority.
Trust is not static.
Neither should authority be.
Explore AIM
aimAdaptive Integrity ModelEvidence
What do we know?
Trust
What confidence does the evidence justify?
Authority
What should be permitted?
Action
What happens next?
Outcome
What actually happened?
Updated trust
What should change?
See why AI was allowed to act.
Real interface. Sample data. Real explainability.
From signals
to authority.
In real time.
The actual AIM interface, shown with sample data.
Evidence
Real signals. Real sources.
Top Drivers
All Signals ⌄outcome_success
Payment system
consistency
Governance log
peer_validation
Support lead review
contradiction
Payment processor
Recent evidence
View all →Refund settled
2 minutes ago
Actions matched policy (40 of 40)
1 hour ago
Reviewed by support lead
1 day ago
Refund reversed by processor
21 days ago (decayed)
Trust State
Dynamic. Always updated.
Agent
Billing Refund Agent
AI agent
decision type: refund
Refund settled. Trust state updated.
Signal Impact
Authority
What should be permitted?
Proposed action
issue_refund · $180
Authority Status
EXECUTE
Within envelope: amount ≤ $1,000
Policy: refunds v1.0, rule 3 (trust ≥ 60 and amount ≤ $1,000)
Rejected alternatives
ESCALATE: amount is below the $1,000 human-review threshold
BLOCK: trust is above 40
Why?
Clear and human-readable.
Why the agent was allowed to act
Three positive signals support trust.
One older contradiction, decayed.
Policy refunds v1.0, rule 3 matched: trust ≥ 60 and amount ≤ $1,000 → EXECUTE
"Recent refunds settled cleanly and the amount is inside the agent's envelope. Anything above $1,000 would go to a human."
Trust Lineage™
Complete and auditable.
Trust Lineage™
Decision created
9/29/2026, 2:02:10 PM
Trust evaluated (70)
9/29/2026, 2:02:10 PM
Authority issued (EXECUTE)
9/29/2026, 2:02:10 PM
Refund executed ($180)
9/29/2026, 2:02:11 PM
Outcome recorded (refund_settled)
9/29/2026, 2:05:40 PM
Trust updated (70 → 72)
9/29/2026, 2:05:41 PM
Every measurement
should be explainable.
Every consequential action
should be traceable.
A more
trustworthy
AI future.
Trust should change
what happens next.
AIM's Authority Gate translates trust state and context into bounded decisions.
Execute
Proceed.
When AIM will execute:
- Trust is high and stable
- No material contradictions
- Within approved scope
- Meets safety and policy rules
Example
Post content to LinkedIn on your behalf.
Constrain
Proceed within defined limits.
When AIM will constrain:
- Trust is moderate
- Some uncertainty or limited evidence
- Allowed in reduced scope
- Additional monitoring applied
Example
Draft an email, but require your review before sending.
Challenge
Require additional evidence.
When AIM will challenge:
- Conflicting signals detected
- Evidence is insufficient
- High-impact decision
- Requires human validation
Example
Pause a financial recommendation and request additional sources.
Escalate
Transfer authority.
When AIM will escalate:
- High-risk or sensitive action
- Significant contradiction
- Outside normal scope
- Requires human decision
Example
Escalate a contract decision to a human team member.
Block
Do not permit the action.
When AIM will block:
- Trust is very low
- Unacceptable risk
- Policy or compliance violation
- Potential harm detected
Example
Block a fund transfer due to suspicious activity.
Trust becomes operational when it changes authority.
A more
trustworthy
AI future.
Don't just record
what AI did.
Preserve why
it was allowed.
AIM connects:
- Evidence
- Trust State
- Authority
- Action
- Outcome
- Updated Trust
Creating a traceable history of how evidence influenced authority over time.
Explore Trust Lineage™01Evidence
What do we know?
02Trust State
What does the evidence indicate?
03Authority
What is allowed?
04Action
What happens next?
05Outcome
What actually happened?
06Updated Trust
What should change?
A MORE
TRUSTWORTHY
AI FUTURE.
Built.
Now ready
to be challenged.
The next milestone
is not another model.
It is evidence.
Built
AIM MVP5
- Signal Store
- Trust Engine
- Trust State
- Authority Gate
- Governance Log
- Trust Lineage™
- API layer
- Human-facing UI
Validating now
The current window
- Enterprise pilots
- Independent research
- Integration testing
- Adversarial testing
- Human experience validation
Next
Evidence → Scale
- Pilot evidence
- Independent validation
- Production hardening
- Commercial deployment
Don't believe AIM.
Test it.
We are selecting focused environments where AI decisions carry meaningful consequences.
The pilot formula
One workflow.
One authority problem.
One measurable test.
Pilot design depends on the workflow, risk profile and participating organization.
A more
trustworthy
AI future.
01
Simulation
Start safely.
Test scenarios.
02
Sandbox
Integrate and test
in a controlled environment.
03
Shadow mode
Run in parallel.
Compare outcomes.
04
Constrained live
Limited scope.
Real-world validation.
Ideal pilot
Bring us an AI workflow with:
- Meaningful action.
- Available evidence.
- Defined outcomes.
- Measurable risk.
- Human oversight.
- A bounded testing environment.
Pilot conversations are open now.
Measure | Explain | Control
We built
the model.
Help us find
where it breaks.
We are seeking universities, laboratories, and independent researchers interested in testing adaptive trust + authority.
Not endorsing AIM.
Challenging it.
A more
trustworthy
AI future.
01
Research questions
Define hypotheses.
Explore assumptions.
02
Experiment design
Test under real
and adversarial conditions.
03
Analyze results
Measure trust, authority
and failure modes.
04
Publish & share
Independent findings.
Stronger AI for everyone.
Questions worth testing
- Can dynamic evidence produce useful trust states?
- Can trust reliably inform authority?
- How should contradictory evidence change authority?
- How should trust decay?
- When should humans override AIM?
- Can people understand why AIM decided?
- Where does the model fail?
AI Governance · Trustworthy AI · AI Safety · Explainable AI · Human-AI Interaction · Agentic Systems · Decision Science · Computational Trust
Building the layer
Between intelligence
and action.
Veraxius is developing trust + authority infrastructure for increasingly autonomous AI systems.
Our current focus is simple:
- Build.
- Test.
- Measure.
- Learn.
Then scale what the evidence supports.
AIM needs more
than users.
It needs
challengers.
Different perspectives. A stronger, more trustworthy AIM for everyone.
A more
trustworthy
AI future.
Building within
a global AI ecosystem.
Veraxius is a member of the NVIDIA Inception program.
Our participation provides access to NVIDIA's startup ecosystem, technical resources, training, and growth opportunities as we continue developing and validating AIM.
A more
trustworthy
AI future.
AI can act.
The question is
whether it should.
AIM is ready for its next test.
Trust must be measured.
Authority must be earned.
A more
trustworthy
AI future.