ASI provides a multimodal AI engine that integrates literature, experimental metadata, and multi‑omics data into a unified knowledge graph, enabling context‑aware hypothesis generation and predictions across RNA, protein, metabolite, structural, and clinical domains. The platform offers an interactive AI scientist interface for query‑driven exploration, continuously updating the knowledge base as new data become available.
Funding
Funding not disclosed
Founders
Product
Problem
Biological research generates diverse data types—RNA, proteins, metabolites, structural information, and clinical outcomes—but current AI tools process only a single modality, leading to incomplete analyses and missed experimental context.
Solution
ASI offers a multimodal biological answer engine that aggregates literature, experimental metadata, and multi‑omics signals into a unified, structured knowledge system. By integrating all data modalities in real time, the platform enables reasoning rather than simple retrieval, allowing researchers to generate predictions that respect the full cellular context. The engine continuously updates as new data are added, providing a persistent knowledge layer that supports hypothesis generation, experimental design, and translational decision‑making. Users interact with an AI‑driven scientific assistant that surfaces relevant insights across RNA, protein, metabolite, structural, and clinical domains without manual data stitching.
Target Audience
Primary users are biomedical researchers, drug discovery teams, and translational scientists who need integrated, context‑rich insights across multiple biological data types.
Features
- Unified knowledge graph linking literature, omics datasets, structural models, and clinical records across all biological modalities
- Real‑time multimodal reasoning engine that generates context‑aware hypotheses and predictions
- Extraction and incorporation of experimental metadata (cell line, protocol, timepoint, lab conditions) from publications and lab notes
- Interactive AI scientist interface for query‑driven exploration and iterative hypothesis refinement
- Scalable compute infrastructure leveraging GPUs to process large omics and structural datasets efficiently
- Persistent, versioned knowledge base that evolves with ongoing research inputs