Building frontier AI systems for scientific discovery and deep technology
We develop AI-driven solutions across remote sensing, biotechnology, and autonomous intelligence, advancing capabilities at the intersection of machine learning and applied science.
Explore domainsBORMOS sits at the convergence of artificial intelligence and scientific discovery: a deep tech R&D company building systems that address fundamental challenges.
Our research spans AI-driven remote sensing, computational biology, and autonomous agent architectures. We develop models and systems that push the boundaries of what AI can achieve in applied science, from satellite imagery analysis to drug discovery pipelines.
We bring deep technical rigor and research-grade engineering to every domain we enter. Our team operates at the intersection of machine learning research and production systems, translating scientific breakthroughs into real-world capabilities.
PyTorch
Hugging Face
NVIDIA
Databricks
Docker
MLflow
Ray
OpenCV
LangChain
Kubernetes
TensorFlow
W&BPyTorchHugging FaceNVIDIADatabricksDockerMLflowRayOpenCVLangChainKubernetesTensorFlowW&B
AI-Driven Remote Sensing
Satellite imagery analysis, geospatial AI, and environmental monitoring. We build models that interpret Earth observation data at scale for climate, agriculture, and defense.
AI in Biotechnology
Computational biology, drug discovery, and genomic analysis. Our systems accelerate research in molecular modeling, protein structure prediction, and biomarker identification.
Agentic AI Systems
Autonomous agent architectures, multi-agent orchestration, and intelligent decision systems. We design agents that reason, plan, and execute complex tasks independently.
Deep Tech R&D
Fundamental technology research: novel architectures, optimization methods, and cross-domain AI applications. Long-horizon research creating foundational capabilities.
01Research
Rigorous scientific inquiry: reviewing literature, identifying where AI creates breakthrough impact, formulating hypotheses against real-world constraints.
02Build
Custom model architectures validated against real-world data. Rapid experimentation cycles with research-grade engineering and reproducible pipelines.
03Deploy
Production systems with robust monitoring, continuous evaluation, and feedback loops. Research that ships, reliable, scalable, maintained.
