Team Recre
Connected
Evidence
Features
Knowledge Graph Database
From approximately 40 million medical papers, we construct a Knowledge Graph Database based on both semantic meaning and citation relationships.
Our system goes beyond evaluating papers one by one. It analyzes how each paper is connected across context, time, and the broader structure of medical knowledge.
Within the generated subgraph, the system identifies core papers that hold influence in the field and act as key junctions in the flow of scholarly discussion.
Question-Based Subgraph
We offer more than a tool for efficiency.
By analyzing the connectivity between papers in medical literature databases, we build a unique Knowledge Graph Database that is not dependent on human subjectivity.
In response to each user’s question, our system dynamically reconstructs only the relevant portion of this vast network. It filters out noise and identifies the core papers that truly deserve attention.
From there, it instantly generates answers grounded in reliable evidence.
We bring an end to time spent searching, and create more time for deepening knowledge.
This is the future our technology makes possible.
Dynamic Graph RAG
Our system identifies core papers that are highly relevant to the user’s question and provides them to the AI as trusted source material. This enables the AI to generate answers grounded in reliable evidence, reducing the risk of hallucinations.
Product Differention
01
Proprietary Paper Selection Algorithm
Through our proprietary citation-based database and paper selection algorithm, our service identifies the papers that can most accurately answer each user’s question.
02
Model-Agnostic Architecture
By separating the LLM from the knowledge source, we establish accuracy that is not dependent on any specific AI model. Our proprietary retrieval algorithm extracts core knowledge from the literature, enabling reliable performance across different models.
03
A Bird’s-Eye View of the Literature
Instead of showing related papers as a simple list, our system reveals how research has evolved over time within the relevant field and highlights the core papers at the center of that progression.
The Problem
Medical literature is expanding at an overwhelming pace. For clinicians and researchers who need to stay up to date, reading every relevant paper and verifying the reliability of each finding has become practically impossible.
General-purpose AI remains limited by hallucinations and insufficient grounding in reliable sources. At the same time, many keyword-based search systems depend only on word matching or semantic similarity, often returning noisy or irrelevant results.
We provide a solution that enables users to quickly access reliable information that keyword search alone cannot reach.
Solutions
We offer more than a tool for efficiency.
By analyzing the connectivity between papers in medical literature databases, we build a unique Knowledge Graph Database that is not dependent on human subjectivity.
In response to each user’s question, our system organically reconstructs only the relevant portion of this vast network. It filters out noise and extracts the core papers that truly need to be read.
From there, it instantly generates answers grounded in reliable evidence.
We bring an end to time spent searching, and create more time for deepening knowledge.
This is our technological answer.
