An AI-driven engine built to help researchers accelerate pre-clinical trials, identify critical safety firewalls in silico, and discover novel multi-modal treatment strategies by mapping vectorized clinical data directly to patient biopsy profiles.
Translational Discovery Engine for Multi-Modal Immunotherapy
Architected for precision, grounded in fact.
To rigorously mitigate algorithmic hallucinations and ground the models in clinical reality, the OncoFi engine does not rely on generalized logic. We utilize a highly specialized, retrieval-augmented architecture designed to surface high-fidelity, biologically plausible protocols for expert validation.
Vectorized RAG Pipeline
Retrieval-Augmented Generation synthesizes the latest peer-reviewed research directly into the reasoning pathway before any protocol is generated.
High-Dimensional Data Storage
Powered by a Pinecone vector database, the system instantly cross-references thousands of biomarkers and therapeutic conflicts in silico.
Dynamic Entity Orchestration
A highly modular architecture allows researchers to comprehensively manage and scale core biological variables. Users can seamlessly define and adjust entities like cancer types, CAR-T conditioners, adjuvants, and molecular targets, ensuring the engine continuously adapts to new discoveries.