LynxKite offers a no‑code AI orchestration platform that lets pharma and other data‑intensive teams build, deploy, and manage graph‑native, GPU‑accelerated pipelines using drag‑and‑drop workflow boxes, knowledge graphs, and pretrained models. The platform automates infrastructure provisioning, microservice scaling, and collaboration, enabling faster, reproducible drug discovery and multimodal analytics without extensive coding.
Funding
Funding not disclosed
Founders
Product
Problem
Pharma and other data‑intensive industries struggle to build, scale, and govern complex AI pipelines that combine heterogeneous biomedical data, knowledge graphs, and GPU‑accelerated models, often requiring extensive coding and manual infrastructure management.
Solution
LynxKite provides a composable, no‑code AI orchestration platform that lets users assemble reusable workflow boxes from existing components, Python code, LLM agents, and external tools. Its graph‑native interface represents relationships in data and enables the integration of biomedical knowledge graphs, NVIDIA BioNeMo models, and graph neural networks for multimodal analytics. Users can design drag‑and‑drop pipelines, deploy them with a single click to GPU‑accelerated environments, and let the platform automatically manage inference microservices, reducing operational overhead and accelerating preclinical and early‑stage R&D.
Target Audience
Primary customers are pharmaceutical R&D teams—including data engineers, data scientists, and domain scientists—who need to build and scale AI‑driven drug discovery workflows, as well as enterprises in finance and retail seeking graph‑based generative AI solutions.
Features
- Drag‑and‑drop workflow builder that supports code, LLM agents, and third‑party tools without requiring programming
- Graph‑native modeling with built‑in support for knowledge graphs, GNN training, and over 600 graph algorithms (including 100+ GPU‑accelerated via cuGraph)
- Seamless integration of NVIDIA BioNeMo, RDKit, custom LLM APIs, and vector/graph databases for multimodal data processing
- One‑click deployment to Kubernetes or cloud clusters with automatic start/stop of GPU inference microservices to optimize resource usage
- Visual analytics for network patterns, model parameters, and results, enabling interpretation and decision support
- Multi‑user collaboration workspace with shared pipelines, versioning, and plug‑in extensibility for custom nodes or scripts