Off-the-shelf models don’t work on biological data. We build custom deep learning systems for your specific organism, phenotype, or dataset, and deploy them where they need to run.
What We Do
- Deep learning model development (CNNs, transformers, multi-task networks)
- Computer vision for biological imaging (RGB, infrared, ultrasound, microscopy, satellite, drone)
- Reinforcement learning systems and MDP-based agent design
- Model training on HPC clusters (SLURM, GPU scheduling)
- MLOps and model deployment (cloud and edge)
- Benchmarking, evaluation, and interpretability
Deliverables
- Trained model with documented accuracy, precision, recall, and domain-specific performance metrics
- Inference API or deployable model package (Docker, TensorRT, ONNX, or TFLite)
- Training pipeline with versioned data splits for reproducibility
- Model card documenting architecture, training data, limitations, and intended use
- Integration guide for your existing data infrastructure or application
Need a model built for your data? Let’s talk.
