A public-data intelligence studio for pharma questions.
Lippershey builds source-linked intelligence platforms for pharma, biotech, market access, portfolio, competitive intelligence, and innovation teams. We turn fragmented public data into structured research workflows, transparent evidence views, and decision-support outputs that users can inspect, question, and validate.
What Lippershey is
Lippershey is a public-data pharma intelligence platform builder. It combines public sources, deterministic workflows, and AI-supported interpretation to help teams investigate strategic questions across clinical development, patents, market access, technology landscapes, and portfolio signals.
Each platform is designed around a question:
- What changed?
- Which signals are visible?
- Which public sources support the finding?
- What remains uncertain?
- What should a human expert review next?
Lippershey does not try to hide uncertainty. Missing, incomplete, stale, or ambiguous evidence should be shown clearly rather than treated as invisible.
Why the name Lippershey
The name Lippershey is inspired by Hans Lippershey, the Dutch-German spectacle maker traditionally associated with one of the earliest known patent applications for the telescope in 1608.
The telescope did not create the stars. It changed what people could observe, compare, and question.
Lippershey follows that same idea for pharma intelligence. The platform does not invent evidence or replace expert judgment. It helps users look more clearly at public data, connect scattered signals, and inspect the sources behind an observation.
A better instrument for asking questions
Pharma teams work with more public information than ever: clinical trial registries, publications, regulatory documents, patent records, HTA decisions, company disclosures, and scientific literature.
The problem is not only access to data. The problem is turning fragmented public evidence into a structured view that supports serious questions.
Lippershey was created to make public-data intelligence more transparent, reusable, and inspectable. Each platform starts with a focused question, maps the relevant public sources, applies clear rules, and produces outputs that users can review rather than blindly trust.
The goal is not to automate judgment. The goal is to give experts a better instrument for asking questions.
About Sebastian Azar
Sebastian Azar is a pharma technology and digital innovation professional based in Woerden, Utrecht, Netherlands. His work sits at the intersection of digital health, patient-centric innovation, emerging technologies, data-driven operating models, and new business models for life sciences.
He has held roles across digital health and advanced technology innovation, IT business partnering, CRM, customer operations, and innovation delivery. His experience includes work with AstraZeneca, Alexion Pharmaceuticals, Align Technology, and ASW, with responsibilities spanning digital innovation, technology strategy, stakeholder alignment, and business-facing IT delivery.
Sebastian's background combines pharmaceutical science, healthcare AI learning, industrial biotechnology, OKR practice, and hands-on experience in building technology-enabled business concepts. He is currently completing AI engineering training covering Python, NLP, Transformers, LLMs, LangChain, Hugging Face, and APIs, and is consulting several companies on IT and Digital Innovation.
Lippershey reflects that combined perspective: pharma domain understanding, technology execution, public-data analysis, and a belief that intelligence products should remain explainable, source-linked, and useful to human decision-makers.
- Associate Director — Digital Health & Advanced Technology Innovation
- IT BP Innovation Senior Manager
- IT BP Digital Manager
- Alexion Pharmaceuticals — Information Technology Business Partner
- Align Technology — CRM Senior Manager
- ASW — CRM Senior Manager
- Atlantic Technological University — BSc in Pharmaceutical Science
- Taipei Medical University — Artificial Intelligence for Healthcare: Opportunities and Challenges
- Industrial Biotechnology
- OKR Coach Certification
- Currently completing Complete AI Engineer Training (Python, NLP, Transformers, LLMs, LangChain, Hugging Face, APIs)
- Publications on De-Sci and biopharma IP-to-NFT transfer
- Volunteering: Swiss Red Cross volunteer; blood donor
How Lippershey works
- Step 01Start with a question
Every platform begins with a specific pharma intelligence question.
- Step 02Map public sources
Relevant public sources are identified, named, and scoped.
- Step 03Apply transparent rules
Data is normalized, classified, filtered, or scored using visible logic where possible.
- Step 04Use AI carefully
AI may help structure, summarize, and connect evidence, but should not replace source inspection or human judgment.
- Step 05Show uncertainty
Missing, stale, ambiguous, or incomplete evidence should be visible.
- Step 06Require human review
Outputs must be reviewed and validated by the user before use. Lippershey surfaces public-data intelligence; it does not make decisions or provide professional advice.
What Lippershey does not claim
Lippershey products and outputs are for research and informational use only. They are not:
- Medical advice
- Clinical advice
- Financial or investment advice
- Legal advice
- Regulatory advice
- A substitute for professional judgment
- A prediction, probability, or validated risk score unless explicitly stated and independently validated
Public-data coverage may be incomplete, stale, ambiguous, or incorrect. Users are responsible for validating sources, assumptions, and decisions before relying on any output.
Build the intelligence platform your question requires.
Start with a focused pharma intelligence question. Lippershey can help scope the sources, filters, outputs, and transparency layer needed to turn public data into a reviewable research workflow.