Stop guessing how good your medical AI is. Start proving it.
Good AI output isn’t measured. It’s defined. IQC gives your experts the framework to define it and enforces it automatically on every output, in real time — with a full audit trail that speaks for itself.
Your medical AI passed every test. Then it met the real world.
There’s a gap between knowing a good AI output when you see it and being able to reproduce it every time.
Everyone interprets the requirements differently. Teams stall. And while they debate, AI keeps running.
When your AI is a black box, every output is a liability you can’t see coming.
Investors don’t just bet on your AI. They bet on your ability to control it. Without proof, it’s not a product, it’s a prototype.
The quality layer your medical AI is missing
platform
IQC — Integraded Expert-Anchored Quality Control for Medical AI
IQC closes that gap by embedding a quality layer directly into your Medical AI, turning your experts’ judgement into a standard that scales with every output.
How it works
Your experts define what good output looks like
No lengthy setup. Your experts provide existing guidelines or a few real-world examples. That becomes your gold standard.
Quality gets a definition
IQC analyzes expert responses against criteria like clarity, accuracy and correctness fully adjustable to your use case.
Your team sees quality in real time
Every output is measured automatically against your gold standard. A live indicator shows exactly where it holds up and where it does not. When it falls short, your experts are prompted directly. They review and correct without any technical knowledge needed.
Your experts refine, IQC learns. Quality compounds.
When an expert revises an output, that correction flows back into the system as implicit feedback, continuously refining the standard. If quality improves, IQC advances. If it doesn’t, IQC rolls back to the previous version automatically.
Every decision documented and audit-ready
IQC automatically documents every AI output, every expert revision and every quality decision, creating a full audit trail without any extra steps. Built-in compliance support for EU AI Act and MDR requirements, including Human-in-the-Loop documentation and accuracy assessments.
Everything you need to stay in control of AI quality
Real-Time Output Validation
Know the quality of every output before it reaches the wrong hands.
Expert-Driven Benchmarking & Thresholds
One consistent standard across every model, every use case, every team.
Automatic Rollback
No quality regression goes unnoticed or uncorrected.
Human-in-the-Loop Control
Your experts can review, correct, and override directly in the workflow. Designed to counter automation bias and keep human judgment where it belongs.
Continuous Quality Monitoring
Quality doesn’t stop after deployment. IQC keeps measuring, every output, every time. Supporting post-market surveillance requirements.
Automated Compliance Documentation
Every decision is documented automatically. Ready for EU AI Act and MDR audits.
Platform & Integration
A dedicated interface for your team, scalable across use cases and built to fit into your existing systems.
Trusted by institutions, recognized by the industry
ailusive is a Fraunhofer spin-off backed by Fraunhofer Venture, built on 50+ years of combined expertise in AI quality evaluation. IQC is currently being implemented on an AI platform for the German Federal Ministry for Digital and State Modernisation. As active contributors to ISO standards and the EU AI Act, we don’t just build for compliance, we build for the people at the center of every AI output. Named winner of the Road to START Summit pitch contest in Nuremberg, competing against seven startups at ZOLLHOF Tech Incubator.
Latest Publications
We actively contribute as experts to the research community
Which Method(s) to Pick when Evaluating Large Language Models with Humans? – A comparison of 6 methods
Human evaluations are considered the gold standard for assessing the quality of NLP systems, including large language models (LLMs), yet there is little research on how different evaluation methods impact results. This study compares six commonly used evaluation methods – four quantitative (Direct Quality Estimation, Best-Worst Scaling, AB Testing, Agreement with Quality Criterion) and two qualitative (spoken and written feedback) – to examine their influence on ranking texts generated by four LLMs…
Designing Usable Interfaces for Human Evaluation of LLM-Generated Texts: UX Challenges and Solutions
Human evaluations remain important for assessing large language models (LLMs) due to the limitations of automated metrics. However, flawed methodologies and poor user interface (UI) design can compromise the validity and reliability of such evaluations. This study investigates usability challenges and proposes solutions for UI design in evaluation LLM-generated texts. By comparing common evaluation methods, insights were gained into UX challenges, including inefficient information transfer and poor visibility of evaluation materials…
The window to lead in medical AI is now
Medical AI is moving fast. Regulations are catching up. And the gap between deploying AI and controlling it has never been more consequential.
Institutions that can’t demonstrate control over their AI outputs won’t just face audits. They’ll face decisions about whether to continue.
ailusive was built for this moment. As a Fraunhofer spin-off, we’ve spent years understanding what it takes to make AI reliable in practice.
Our vision is a trustworthy digital future. Our mission is transforming elusiveness into clarity. Expert-driven AI control is how we get there.
Become a pilot partner
We are currently onboarding a limited number of pilot partners.
If you’re building or operating Medical AI and want to be among the first to embed expert-driven quality control, let’s talk.