AI Consulting for Life Sciences

With the strictest defaults we can engineer. As a German company we apply European data-protection standards (GDPR) to every engagement worldwide: data processing agreements, data minimization, pseudonymization or anonymization wherever the science allows, and architectures where sensitive data stays inside your environment. For LLM-based tools we deploy private, hosted models, so protocols, patient narratives, and proprietary knowledge are never sent to third-party APIs. Where feasible, we can also develop against de-identified or synthetic data before any production data is touched.

Yes — with discipline. Many high-value use cases sit adjacent to regulated processes (literature screening, document triage, internal assistants) and can be deployed with a human firmly in the loop. Where a model supports quality or clinical decisions, we design for the expectations of regulated environments: documented datasets and training procedures, versioned models, defined performance acceptance criteria, and audit trails.

To be clear about what we do not do: we do not issue regulatory certifications, and we never claim a system is "GxP certified" — no software vendor honestly can. What we deliver is a solution engineered and documented so that your quality and regulatory teams can validate it within your own framework, and we work alongside them to get there.

We treat validation as a first-class deliverable, not an afterthought. That means a locked, held-out test set your team helps define; predefined acceptance metrics agreed before training begins; systematic error analysis showing not just how often the model fails but how it fails; reproducible pipelines so results can be regenerated on demand; and documentation written for scientific and QA readers, not just developers. The goal is that your reviewers can interrogate the method the same way they would interrogate an assay.

Usually, yes. Small, well-curated datasets are the norm in research, and modern methods are built for them: transfer learning from large pre-trained models, data augmentation, and careful cross-validation protocols routinely deliver strong results from hundreds rather than millions of examples. Our ocular volume estimation project is exactly this pattern — research-grade deep learning on specialized medical scan data.

The honest caveat: sometimes the data genuinely is not enough, and the right answer is a targeted annotation effort or a redesigned collection protocol before modeling. We assess that in the discovery phase and tell you straight — before you fund development.

Yes — integration is where AI stops being a demo and starts being a tool. We build against your existing landscape: LIMS and ELN platforms, imaging archives, document management systems, and clinical data stores, typically via their APIs or export interfaces. Deployment can be on-premises, in your private cloud, or in an EU-hosted environment, depending on your data governance requirements. We deliberately avoid architectures that lock you into our involvement: your IT team receives the documentation and handover needed to own the system.

Both, depending on the claim. AI will not invent a blockbuster molecule on its own — anyone promising that deserves skepticism. What it demonstrably does is compress the expensive, repetitive parts of discovery and development: screening literature and patents at scale, prioritizing candidates from assay and screening data, quantifying images that previously required expert reading, and structuring the unstructured data your pipeline generates. With roughly 90% of clinical-stage candidates failing, the economic case is not "AI finds the drug" — it is "AI helps your scientists kill bad options earlier and pursue good ones faster."

Because your problems are scientific problems before they are software problems. A Ph.D.-trained consultant understands experimental design, statistical power, confounders, and the difference between a validation result and a lucky test split — so the models we deliver hold up when your scientists push on them. Just as importantly, they speak your reviewers’ language: methods sections, error bars, and reproducibility are our native habitat, which shortens the path from prototype to internal acceptance considerably.

A well-scoped proof of concept on your own data typically takes weeks, not months — long enough to produce honest performance numbers, short enough to keep the decision cheap. From there, our incremental path (PoC → MVP → production) means an MVP integrated with your systems usually follows within a few months, with validation and documentation developed in parallel rather than tacked on at the end. Regulated deployment contexts add review cycles, which we plan for explicitly with your QA and IT teams during scoping.

Pricing depends on the complexity of the problem, the state of your data, and how deeply the solution must integrate and be validated. AI Superior offers fixed AI development plans with a guaranteed outcome at a predefined price — a model that suits regulated organizations well, because every stage is a bounded, separately approved commitment rather than an open-ended research budget. Contact us for a quote based on your specific project.

Yes. We are based in the Frankfurt Rhine-Main region (Darmstadt) with a second office in Berlin, and we work with clients internationally. Projects run remotely with structured communication at every stage, and EU-based delivery is a practical advantage for organizations that need GDPR-grade handling of research and clinical data regardless of where they operate. Reach us at info@aisuperior.com or +49 6151 7076909.