About

Regulatory intelligence in support of your expertise

RAFI stands for Regulatory Affairs Factual Intelligence. RafiHive brings that purpose to EU pharma regulatory affairs: helping professionals work with regulatory evidence while keeping their expertise, judgment and accountability at the centre.

Our mission

To give regulatory-affairs professionals more time to apply their expertise by helping them find, understand and review regulatory information. We support research, evidence review and drafting with cited sources, explicit assumptions and visible gaps, so RA teams can focus on interpretation, strategy and informed decisions.

Our vision

A future where every regulatory-affairs professional has accessible, traceable intelligence at hand, and expert judgment remains at the heart of every regulatory decision. RafiHive exists to support RA professionals, not replace them.

Why we built it

Regulatory guidance is fragmented across EMA, the European Commission, EUR-Lex, CMDh, EDQM, eSubmission/SPOR and national authorities, and it changes over time. Finding and comparing the relevant evidence takes time. RafiHive helps organise that research into cited answers that distinguish law, guidance, Q&A, draft and superseded material for professional review.

A European product, built in Germany

RafiHive is designed, built and operated in Germany for EU human-medicines regulatory affairs. It is built for the EU regulatory framework from the inside: EU procedures, EMA and CMDh practice, and national requirements are the starting point, not a localisation of a product designed for another market.

Hosting, storage and AI inference stay inside the EU, personal data is processed under the GDPR, and the terms are governed by German law. Details are on the security page.

Regulatory-affairs expertise shapes the source set, workflows, evaluation criteria and product decisions alongside engineering, so domain knowledge is part of the product rather than an afterthought around a generic AI layer.

Our principle

Factual intelligence starts with evidence that can be checked. AI can make mistakes, so every output needs qualified review. You assess source relevance, resolve uncertainty and own the final decision.