Static policies fail in dynamic AI environments. You need active enforcement at every node.
Every governed request passes through this stack — no exceptions
Thought-Observation-Action loops verify every LLM claim against grounded enterprise data before it reaches users. Catches hallucinations at the source — not in the boardroom.
Centralized PII and PHI redaction across every query and response. GDPR compliance enforced at the infrastructure layer — without custom middleware per application.
Executable rules with human-in-the-loop triggers for high-stakes actions. Policy changes deploy in seconds across the entire AI estate without touching application code.
Detects prompt injection, jailbreak attempts, and policy-violating requests in real time. Behavioral pattern analysis flags misuse before it reaches the model — keeping your AI deployment resilient against adversarial actors and accidental boundary violations.
Manages the full lifecycle of AI tools and skills: discovery, authorization, version control, and deprecation. Native support for MCP and Agent-to-Agent (A2A) protocols.
Abductive Logic Programming tests proposed actions against rival hypotheses. Shifts AI decisions from lexical correctness to causal soundness — verifiable and explainable.
Every layer of the agentic stack is locked down — from credentials to human oversight.
Unique credentials for every user, agent, and sub-agent (NHIs) stored securely in a dynamic vault — no static API keys embedded in code.
Access rights granted only when needed and revoked immediately after, preserving least-privilege alongside RBAC and strong authentication.
A curated registry of vetted APIs, data sources, and tools ensures the AI agent only operates with trusted, secure ingredients.
An overarching inspection layer monitors all inputs and outputs, blocking prompt injections, data leaks, and improper system calls in real time.
Tamper-proof logs that attackers cannot alter or delete, providing full traceability of every agent action and decision.
Continuous scanning across network, endpoints, and AI models themselves to surface latent vulnerabilities before they can be exploited.
Kill switches to stop runaway agents, activity throttles to cap harmful actions, and canary deployments to validate behavior before full rollout.
We combine the reasoning power of Large Language Models (LLMs) with the absolute precision of Knowledge Graphs. Our engine injects symbolic logic into neural workflows to guarantee compliant outcomes.
Includes best practices for Safe and Responsible AI
Purpose-built for the governance, accountability, and compliance requirements of public institutions — in Switzerland and across the EU.
For cantons, municipalities, federal departments, and public institutions seeking sovereign AI governance aligned with Swiss law and data residency requirements.
For EU public bodies navigating the AI Act and GDPR. Built for transparency, accountability, and compliance-ready AI governance across EU member states.
Reach out to discuss how vlinq.ai can be tailored to your institution's specific requirements.
Request a DemoDeploy in minutes on your infrastructure. Zero-dependency runtime.
Engineered with Swiss precision and privacy-first principles.
No lock-in. Works across AWS, Azure, GCP, or on-premise air-gapped systems.
We believe the best software is built in the open. Open source is not a strategy — it is how we work.
vlinq.ai is built on a foundation of world-class open source projects. From our runtime to our security primitives, we rely on the collective intelligence of the open source community to deliver a product that is robust, auditable, and freely inspectable. We are transparent about our dependencies and vet every library we adopt.
We actively contribute upstream to the projects we depend on. Bug fixes, documentation improvements, security patches — if we find something that benefits the community, we send it back. Our engineers are encouraged to participate in open source maintenance as part of their regular work.
Where it makes sense, we release our own tools and libraries as open source. We believe that tooling around AI governance, observability, and safety should be a public good. Releasing our work allows the wider community to audit, extend, and build upon what we create.