Xient Trusted LLM · AI Governance
Which AI can you
trust?
The model landscape changes weekly. We vet the most important language models, assign a Trust Score and say it plainly: this one yes, that one better not. So you decide in minutes what others take months for.
Level up your AI.
Request accessCurrently in private beta - we work with selected pilot customers.
The reality
Models grow wild. Data leaks away.
Developers wire in models because they're convenient right now. Teams test tools in the browser. Hardly anyone systematically checks what a model does with the data, where it comes from and who owns it.
Sprawl
Every team picks differently - no one has the overview.
Data leakage
Sensitive content ends up in third-party services, somewhere in the world.
No evidence
No one can show why a model is secure and compliant.
The shift
Don't guess. Know whom you trust.
Instead of scrutinising every model yourself, you tap a database that has already done exactly that. Vetted, rated, classified - with a score that CIO, CISO and data protection can rely on.
A trustworthy map of AI models. Continuously up to date.
The idea
A vetted database of the key models.
We build a growing, continuously updated database of the relevant language models and generative-AI systems - from small and local to large and global. Every model gets a Xient Trust Score: transparent, vendor-neutral, honest. You see at a glance which models you can trust and which you'd better not.
Trustworthy
Securely hosted, clean contractually, technically robust. Use without hesitation - within your own requirements.
With conditions
Solid, but tied to conditions: only for certain data, with additional controls.
Better not
Clear risks: unclear origin, data leakage, missing evidence. We tell you straight.
How it works
From vetting to selection.
Choose on solid grounds, not on gut feeling.
What we check
A catalogue, not a hunch.
Every model runs through the same questions - from security to provenance. Exactly the points that become decisive when it matters.
Cybersecurity & supply chain
How secure is the model itself - attack surface, dependencies, origin of components?
Data residency & hosting
EU, US or third country? Where does the data sit, who has access, what encryption applies?
Training & contracts
Are inputs stored or used for training? Opt-out, data processing agreements, deletion concepts.
Reliability & hallucinations
Where does the model make things up - and how is that caught in critical processes?
Bias & fairness
What distortions does it carry, and are they tolerable for the use case?
Backdoors & provenance
Where does the model come from, what's inside it, whom do we trust and why?
Prompt, API & agentic security
Prompt injection, privilege escalation via agents, insecure tools and function calls.
Operations & evidence
Logging, role and rights management, incident reporting, independent audit reports.
Only when a model passes these questions does it earn a good score.
Chosen - and then?
Use internally or run it in-house.
Some models you use directly in the company. Others you want to run entirely in-house - data and model under your control. We support both. For local operation, we bring the model onto your hardware, such as NVIDIA DGX Spark, optimised for your purpose and without any data leaving the house.
Run a local LLM in operation: Xient Local LLM →Enterprise access
Your rules. On top.
In the enterprise licence model you get ongoing access to the vetted database and its Trust Scores. On top of that you layer your own cybersecurity policies - network, identities, monitoring, approval rules. We provide the vetted foundation; control stays with you.
How we steer cybersecurity →What the score is based on
Vetted means vetted.
The Trust Score isn't a gut feeling but a robust procedure - oriented to the European standards for trustworthy AI, data protection and information security. Operationalised, not just plastered on a poster.
Xient vets, rates and prepares - we are not an accredited certification body. Certificates are issued by accredited bodies such as TÜV, DEKRA or SGS. Concrete certifications are in preparation.
Why Xient
AI governance, technically solid.
Others deliver a policy or a certificate. We vet the models themselves - technically, transparently, against a fixed catalogue. That comes from our history in data, governance and cybersecurity - and from our own productive AI practice.
Governance
Requirements translated into a verifiable procedure - not just documented.
Security
Technical vetting of models, APIs and agentic workflows - down to the prompt level.
Independent
Vendor-neutral. We assess - you decide, on solid grounds.
Not just policy, not just PowerPoint, not just a certificate - but real vetting of models, data flows, APIs, access and operations.
FAQs
What decision-makers ask first.
What do we get in the enterprise access?
Ongoing access to the vetted database and the Trust Scores - per model with strengths, risks and conditions. On top of that you layer your own cybersecurity policies. You select on solid grounds instead of scrutinising every model yourself.
Which models and providers do you vet?
Established cloud providers as well as local and open-source models - from the small model to the large enterprise LLM. Vendor-neutral, by the same catalogue, continuously expanded.
Can we have our own or local models vetted?
Yes. Local models in particular are strong for sensitive cases, because data and model stay in-house. We vet them by the same criteria as large cloud models.
Do you issue certificates?
No. We vet, rate and prepare - and make you audit-ready. The actual certification is issued by accredited bodies such as TÜV, DEKRA or SGS.
How does this relate to Xient Bridge and Xient Local LLM?
Xient Trusted LLM tells you which models you can trust. Xient Local LLM brings a chosen model securely in-house. Xient Bridge brings governed AI to your vetted SAP data. Three building blocks of the same governance.
Choose AI you can trust.
We'll show you the vetted database, the Trust Score on your models - and how, in the enterprise access, you layer your own rules on top.