Your copilot for clinical real world data applications

Win with Hypercube

Command the full spectrum of your data, structured and unstructured alike, to deliver blazing-fast insights and answer previously unanswerable questions.

Patient Cohorts

Effortlessly sift through millions of patient records to discover the cohort that matches your criteria, all at the pace of a simple conversation. No more wrestling with business rules, learning code, or brute-forcing your way through.

Into Your Clinical Data

After identifying your cohort, unleash your curiosity. Delve deep to get the comprehensive understanding of real-world outcomes your organization needs, from clinical to commercial.


Not a Black Box

With Hypercube, you're not left in the dark. Validate the accuracy of the results by referencing the exact deidentified evidence behind each patient identification.

Not a Standalone Silo

Seamlessly upload and consolidate data from various sources to map out complete patient journeys. Benefit from data pre-loaded by our premier partners, or effortlessly integrate our insights into your existing system.

Years in the Making

AI has been focused on a technique called deep learning – and so far it fails to understand clinical context. Since 2016, we have been developing a hybrid model that couples machine learning with symbolic AI. Symbolic AI is a set of processes common in logic, mathematics and computer science that treat clinical reasoning like a kind of algebra – and the results speak for themselves.

System / F1
Quality (F1) 
Processing Time 
Estimated Cost per Query
0.012 sec.
15,800 sec.
25,300 sec.
Chunks of documents 
Spark NLP
Chunks of documents
Full patient records
Document by document
Related publications

Hypercube is Superior to SQL for Cohort Selection

In an analysis of 100 cohort selection queries, SQL failed to retrieve any patients in 60% – even though eligible patients existed in the data.

Not Just Any AI, but Clinical AI

Learn about the technology
Zero hallucinations

In the AI, "hallucinations" are scenarios where models produces outputs that aren't factual, accurate, or are entirely fabricated. Mendel's Hypercube is designed for unparalleled fidelity and unwavering consistency.



Efficient with both structured and unstructured data

80% of clinical data is unstructured. Extracting insights from such data has traditionally been a cumbersome, non-scalable task, limiting organizations to using about 10% of the available information. Hypercube scales across all data types. From claims to pathology reports, we've got it covered.

Sensitive to clinical context

Consider a query like "patients without cancer surgery." Traditionally, you'd need to painstakingly define what constitutes cancer surgery. By contrast, Mendel's Hypercube is a best-in-class solution that innately grasps this context.



Clinical data often presents conflicting information. In the same medical record, a doctor’s progress note may say the patient has stage 1 cancer, but the pathology report could refer to a metastatic tumor. Unlike conventional systems that analyze at the document or encounter level, Hypercube uniquely offers a comprehensive patient-level understanding.

A patient-centric approach


How do you handle PHI?
We have a robust de-identification engine that hits the 99% HIPAA threshold. We deploy it in your environment so we never see PHI.
What are the data rights that come with Hypercube?
Your data is your data. Because we do the de-identification in your environment, we never see the PHI.
What therapeutic areas do you cover?
We are focused on oncology and immunology. If you’re interested in other areas, let’s chat.
What is the difference between ChatGPT and Hypercube?
Hypercube is clinical-specific and built to reason over health data. This means it does not hallucinate and understands patient-level data.

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