SAVE is our AI platform that identifies promising molecules and indications and prepares them for regulatory approval.
S
earch
Surface high-potential opportunities from untapped scientific evidence.
Our Search layer:
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aggregates heterogeneous biomedical sources (structured + unstructured)
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applies natural language processing (NLP) to extract disease–mechanism–molecule signals from unstructured text
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structures and prioritizes dispersed scientific evidence into traceable opportunity sets
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integrates unmet-need signals and a pharmaco-economic potential lens to support prioritization
Output: structured evidence packages and prioritized candidate opportunities for expert review.
A
ccelerate
Turns regulatory and scientific evidence into execution-ready documentation, while building proprietary IP around a closed, regulatory-grade RAG architecture.
At its core, Accelerate relies on a proprietary Retrieval-Augmented Generation (RAG) combining a protected, in-house recipe of cloud infrastructure (secured AWS environment, including AWS HealthLake compatible data storage), structured knowledge layers, and fine-tuned language models specialized in regulatory writing and dossier logic. All data and workflows operate within a closed, controlled environment, ensuring traceability, confidentiality, and IP accumulation.
Accelerate reduces manual workload and variability through a document automation pipeline from source extraction (OCR when required) and secure cloud storage, to RAG-grounded drafting and workflow orchestration via a web interface.
Where applicable, Accelerate supports regulatory frameworks such as FDA Orphan Drug Designation (ODD) by generating standardized, dossier-ready drafts aligned with expected formats.
V
alue
Prioritize programs that are both medically meaningful and strategically executable.
The Value layer supports disciplined selection by comparing opportunities across:
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strength and consistency of the evidence base
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clarity of mechanistic rationale
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feasibility signals and pathway readiness
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strategic attractiveness informed by pharmaco-economic potential
Output: ranked program candidates with clear rationale to support internal decisions and partner discussions.
E
mbrace
Ground programs in real-world disease context through expert input and feedback loops.
Embrace ensures opportunities reflect clinical reality by incorporating:
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disease-specific clinician and specialist review (relevance, feasibility, care pathway context)
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structured documentation, traceability, and versioning of outputs over time
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iterative improvement through internal review and external feedback where applicable
Output: clinically credible programs designed for real-world execution.