Research
The open questions behind trustworthy AI.
Proofence treats trust as a research problem, not a marketing claim. Our directions explore how AI decisions can be verified, explained, and governed in ways teams can actually rely on.
Research areas
Where we focus.
These are the problems that decide whether an AI decision can be trusted. Each area shapes how the Proofence Engine reasons about a claim, an action, or a policy.
Trust-score calibration
Making a verification score mean what it says—so that confidence tracks genuine reliability rather than the fluency of a model’s output, and drift is detected and communicated honestly.
Multi-model consensus
Treating agreement and disagreement across independent models as evidence about a claim, rather than averaging away exactly the divergence that should raise a flag.
Evidence grounding
Tying AI claims back to identifiable supporting sources and preserving provenance, so a verdict can be inspected and, if needed, contested on its merits.
Policy orchestration
Expressing organizational standards for acceptable AI behavior once and enforcing them consistently at decision time, with the reasoning behind each outcome recorded.
Human-in-the-loop review
Deciding when verification should defer to human judgment, and routing uncertain or high-stakes cases to reviewers with the context they need to act efficiently.
Agent governance
Evaluating an autonomous agent’s proposed action against policy before it takes effect, so governance happens ahead of an action rather than after it has already occurred.
Research directions
What we are working on.
Early-stage directions and positions from the Proofence research organization. Status reflects where each piece of work stands, from concept to published position.
- MethodIn review
Calibrating Trust Scores to Real Uncertainty
Exploring how a verification trust score can be calibrated so that its confidence tracks genuine reliability rather than the fluency of a model’s output.
- MethodConcept
Multi-Model Consensus as a Verification Signal
Investigating how agreement and disagreement across independent models can be used as evidence about the reliability of a claim rather than averaged away.
- FrameworkConcept
Evidence Grounding and Provenance for AI Claims
A conceptual framework for tying AI claims back to their supporting sources and preserving the provenance that makes a verdict inspectable and contestable.
- FrameworkConcept
Frameworks for Policy Orchestration Across AI Systems
Studying how organizational policy for acceptable AI behavior can be expressed once and enforced consistently across many models, products, and decisions.
- PositionPublished
Routing Decisions to Human Review Under Uncertainty
A position on when verification should defer to human judgment, and how to route uncertain or high-stakes cases to reviewers with the right context.
- EvaluationIn review
Governing Agent Actions Before They Take Effect
Examining how to evaluate and authorize the actions autonomous agents take, so each step is checked against policy before it affects the world.
Research meets responsibility.
Our work is guided by how these methods should be used, and by our approach to protecting the core inventions behind them. Both shape what we build.
Bring verifiable trust to every AI decision.
Start in AtlasProof by Proofence, or talk to us about enterprise governance with AirTrustOS by Proofence.