Over 250,000 drug labels exist. The information is there. But it's scattered across PDFs, buried in regulatory databases, locked behind dropdown menus.
Imagine a moment of uncertainty: a patient at home, a pharmacist at the counter, a researcher studying drug safety, or a regulatory professional preparing a compliance report. Each needs answers they can trust.
Not a curated interpretation. Not a summary that might omit critical details. What they need is the actual, regulator-approved text from the official drug label.
The problem isn't the existence of information. It's accessibility. You can't ask a question in natural language and get a direct, trustworthy answer—especially not across thousands of documents at once.
MedCopilot bridges this gap. Ask a question in plain language, and MedCopilot retrieves relevant information directly from authoritative regulatory sources, then generates an answer you can trace back to the exact source text.
We're currently piloting with FDA data: 54,000 deduplicated drug labels covering the US market. But MedCopilot is designed as a global foundation. The same approach works with any regulatory authority's data.
We started with 253,000 raw drug labels from the FDA's public database. After cleaning and deduplication, we identified 54,000 truly unique labels. Each has around 60 fields—dosing, warnings, interactions, contraindications, clinical studies, and more.
When you search for "atorvastatin grapefruit interaction", the system doesn't just search for those words. It understands what you're looking for (a drug-food interaction) and translates that into a targeted search. It looks for the sections of drug labels where interaction information appears.
This is where MedCopilot gets smart. Before running a search, it checks how common each word is across all labels. A very common term might return too many results; a rare term might miss relevant information. MedCopilot dynamically adjusts its search strategy, narrowing or broadening the query to find the most useful matches without overwhelming you with noise.
Once MedCopilot retrieves the relevant label sections, it does two things:
Every fact is traceable. You can click through to see the exact text the answer came from. This transparency is core to MedCopilot's trustworthiness.
Pull from Wikipedia, health blogs, forums. The answer might be accurate—or might blend outdated sources. You can't trace it.
Expert-curated content is valuable, but it's an interpretation of the label, not the label itself. And you navigate through dropdown menus.
Can identify potential interactions not yet documented. Useful for research, but not grounded in regulatory text—a probability, not a citation.
Retrieves from official regulatory text. Every answer points back to that authoritative source. Visible query logic. Complete audit trail.
"What are the FDA-approved indications for atorvastatin?"
Get the precise regulatory text with a direct citation to the source. No ambiguity, no interpretation. Just the authoritative language needed for compliance and verification.
"metoprolol adverse reactions dizziness"
MedCopilot retrieves the relevant adverse reactions sections, presents them in clinical terminology, and cites the exact label passages.
Direct access to regulatory text when clinical judgment matters.
"escitalopram indications anxiety"
The label is already in the box. MedCopilot surfaces the answer in plain language, with a link to the exact passage, so you can verify it yourself.
Label literacy: understand the information you already have.
Unlike typical search tools that give up quickly, MedCopilot tries harder to find your answer.
The system starts by looking in the most likely places—core clinical fields like drug interactions, warnings, and contraindications. If the answer isn't there, it automatically broadens the search to include pharmacokinetics, mechanism of action, and clinical pharmacology. If needed, it searches the entire label—including sections like clinical studies or overdosage information that other tools might skip.
Most systems would simply say "I don't know." MedCopilot keeps looking. The information might exist somewhere less obvious, and finding it could make a difference.
Most AI systems work as a "black box": you get an answer, but you can't see how the system arrived at it. If the answer is wrong, you have no way to understand why, and no way to verify the sources.
MedCopilot is designed differently. Every search is logged and traceable, supporting regulated environments and professional accountability:
You can see exactly which terms the system searched for
You can see which label sections matched and why they were selected
You can click through to the original regulatory text to verify any claim
This matters for regulated environments. When someone asks, "How did you arrive at this answer?", there's a complete audit trail. Not a probability score or a neural network's interpretation, but a documented path from your question to the source text. This auditability is a key credibility signal for organizations and professionals.
Not everyone needs the same level of detail. A patient asking about side effects needs clear language. A pharmacist needs clinical terminology. A medical researcher needs comprehensive data.
MedCopilot adapts its response based on who's asking
Professional terminology, dosing details
Technical depth, preserved complexity
Exhaustive detail, all variations
Plain language, understand your label
To be clear about scope:
MedCopilot surfaces information from official labels; it doesn't recommend treatments or replace clinical judgment.
It retrieves what's in the regulatory text, not predictions about undocumented interactions.
It focuses on the regulatory label text rather than chemical structures, protein targets, or clinical trial registries.
This focused scope is what makes it reliable: you get exactly what's in the authoritative source, nothing more and nothing less. MedCopilot is not a diagnostic tool, but a trusted bridge to regulatory knowledge.
Different tools serve different needs. Prediction systems like DDI-GPT and Decagon discover potential interactions not yet documented—valuable for research but not citeable as regulatory source. Curated databases like Lexidrug, Micromedex, and DrugBank provide expert-written summaries—trusted but one step removed from the original.
MedCopilot answers a specific question: "What does the FDA label actually say?" The answer comes directly from the regulatory text, with citations to the exact row and field. If the label doesn't say it, MedCopilot won't claim it does.
This isn't a limitation—it's the design. For pharmacovigilance teams verifying adverse event reports, medical affairs teams needing exact wording for promotional claims, or prior authorization teams citing FDA-approved indications, the regulatory source is exactly what's needed.
We started with FDA data because it's publicly available, well-structured, and covers a market of over 300 million people. But the real opportunity is broader.
Regulatory authorities worldwide publish similar data. Health Canada, the European Medicines Agency, agencies across Asia, Latin America, and beyond. Each has their own approved formulations, brand names, and sometimes different indications for the same active ingredients.
The platform is built around the concept of regulatory text, not just FDA data. Adding a new country means adding their data, not rebuilding the system. The question-answering logic, traceability, and persona adaptation all transfer.
Generic AI systems trained on US data can't serve international markets. MedCopilot is designed as a truly global solution.
The natural next steps focus on expanding the data:
Health Canada, the European Medicines Agency, and other national authorities. The platform architecture supports any structured regulatory data; adding a new country requires loading their data, not rebuilding the system.
Allow electronic health records and pharmacy systems to query programmatically.
Support specific use cases like pharmacovigilance, label comparison, and regulatory compliance verification.
The core remains the same: authoritative sources, transparent retrieval, verifiable answers. What changes is the breadth of data, and that's by design.
Regulatory authorities publish drug labels because that information needs to be public. But publishing isn't the same as accessible. A document buried on a government website doesn't help someone with an urgent question.
What we built is simple in concept: take that authoritative information, make it searchable by natural language, and show exactly where every answer comes from.
No hallucination. No interpretation drift. No black-box ranking.
Just the information that already exists, made accessible, verifiable, and useful.
MedCopilot invites you to be part of a future where trusted drug information is available to everyone, everywhere.
MedCopilot lets you ask any question about medications in plain language and get answers grounded in official regulatory text, with the ability to expand across countries and authorities.
Retrieves from official regulatory labels, the legally required, regulator-approved text
Every search is logged and traceable; every answer includes source citations you can verify
Same question, different detail, adjusted for clinician, expert, researcher, or patient
Auditable search logic, persistent retrieval that doesn't give up, complete audit trail.
Adaptable to any country's regulatory labels, addressing a global need for localized, authoritative drug information.