We test AI systems for discrimination, explain their decisions in plain language, and turn the result into a proof anyone can check. Expertise that used to take a specialist team, built into tools every team can use.
Based in
Zollikon, Switzerland
Status
Closed beta
Research
IEEE SDS 2026 Best Paper
Standards
W3C · ToIP · DIF
Balanced: three bodies hold one shape, and trust is the orbit they trace.
Mission
AI now decides who gets hired, who gets credit, who gets care.
Almost no one can check whether those decisions are fair. The people who could are few, expensive and busy, so the checking rarely happens, and when it does, the result is a report someone has to take on faith.
validant.ai exists to change that. We make fairness, explainability and verifiable proof reachable for every team that builds or deploys AI, and for every person an AI decides about. Not only to satisfy a regulator, but for a future in which AI agents act on our behalf, and we need to know, and to prove, that they act in our interest.
The only guardrail that scales is one that others can check.
What we believe
Five principles we will not trade for speed.
01
Assessed, not asserted
A claim that an AI system is fair is worth nothing until someone has measured it, with the statistics to say how sure they are.
02
Independent by design
We do not build the models we measure, host your data, or sell the systems we evaluate. We work the way an auditor works.
03
Proof anyone can check
Results become signed credentials a stranger can verify, with no login and without having to trust us.
04
People keep control
The person an AI decides about gets their own record, and authority handed to an agent can always be taken back.
05
Open standards first
W3C credentials and the open work at Trust over IP and DIF come before features. We extend ecosystems, we do not lock them in.
Drawing · hover for the photo
Founder
Daniel Glinz
Founder, validant.ai · Founder, Glinz & Company
Daniel has spent more than twenty years in digital transformation, first with international consulting firms and, since 2014, at the head of Glinz & Company. He founded validant.ai to close the gap between what AI promises and what anyone can check. His research treats digital trust as an architecture of four layers, most of it below the waterline, and the platform turns that architecture into measurements and proofs.
Best Paper Award, IEEE SDS 2026
“The Architecture of Digital Trust: A Multi-Level Framework for Bridging the AI Value Gap”, Zurich, May 2026.
Business administration at the University of St. Gallen (HSG), an MAS in Mobile Application Design at the Zurich University of the Arts, and data at UC Berkeley.
Author of the Signal research series
Essays on fairness, liability and digital trust, from “Bias is the Foundation” to “Precision Is Not Proof”.
Research, engineering and execution. One coherent stack for trust.
Three independent companies, one architecture: iceberg.digital defines what trust is, validant.ai encodes what it does, Glinz & Company embeds it where it matters. You get all three with no seams.
01
iceberg.digital
Research & framework
Defining the structural logic.
Explores sovereignty, governance, identity, reciprocity and institutional design. Trust must be defined structurally before it can be implemented operationally.
02
validant.ai
Engineering & platform
Encoding the systemic capability.
Transforms fairness and governance principles into structured capability frameworks. Trust becomes measurable, verifiable and scalable.
03
Glinz & Company
Operations & advisory
Embedding in enterprise reality.
Partners with executive teams to redesign AI-enabled operating models. Trust becomes anchored in institutional practice.
Why this ecosystem
01Full-pipeline fairness from day one
02Regulatory readiness built in
03Trust grounded in theory, not just tooling
04Enterprise execution, not just advice
Approach
Trust is built. Fairness is proven. Both follow the same four steps.
Our methodology fuses the iceberg.digital four-layer trust model with the Validant fairness lifecycle. One coherent journey from first listen to continuous assurance — auditable at every phase.
01
Understand.
iceberg.digital
Listen and observe before instrumenting. Map the trust cues that already exist, the ones that are missing, and the ones that quietly break.
Trust audit · 10 constructs · 128 cues
Stakeholder mapping
Risk perception baseline
02
Detect.
Validant fairness lifecycle
Quantify what is happening — bias, drift, hidden disparities. Statistical rigor built for a paper, not a press release.
35+ fairness metrics · proxy & subgroup analysis
Bootstrap, Bayesian, permutation testing
Multiple-testing correction
03
Enable.
iceberg.digital + Validant
Build the trust cues and the cryptographic guarantees behind them. Mitigate the bias you measured. Prototype with real users.
12 fair losses · adversarial debiasing
5 calibrators · threshold tuning
Verifiable credentials · agent mandates (planned)
04
Engender.
iceberg.digital + Validant
Operate it. Govern it. Make the assurance ongoing. The work is finished when the next regulator, the next audit, the next user can verify it themselves.
validant.ai is operated by Glinz & Company GmbH Zollikon, Switzerland
FAQ
Frequently asked questions
Who is behind validant.ai?
validant.ai is built in Switzerland alongside two independent partners: iceberg.digital, which researches and defines the digital trust framework, and Glinz & Company, which embeds it in enterprise operating models. validant.ai is the engineering and platform layer between them.
Who founded validant.ai?
validant.ai was founded by Daniel Glinz, who also founded the Swiss digital strategy boutique Glinz & Company in 2014 and has more than twenty years in digital transformation. His paper “The Architecture of Digital Trust”, presented at the 2026 IEEE Swiss Conference on Data Science and AI in Zurich, sets out the framework behind the platform.
Is validant.ai a member of Trust over IP or DIF?
The company is not. Its founder is personally a contributor at Trust over IP and the Decentralized Identity Foundation, and works in their joint Decentralized Trust Graph Working Group, which develops the Verifiable Trust Infrastructure (VTI) specification. He also serves as co-chair of the Human Trust Experience Task Force.
