Tacit Machines provides a data and compute platform that enables researchers to transfer biological experiments from traditional wet‑lab environments to GPU‑accelerated computing clusters. By handling large‑scale biological datasets and offering optimized pipelines for GPU processing, the infrastructure speeds up analysis and modeling of biological systems, allowing scientists to run complex simulations and AI‑driven experiments more efficiently.
Funding
Funding not disclosed
Founders
Product
Problem
Biological researchers often rely on manual wet‑lab protocols and CPU‑bound analysis pipelines, which are slow, difficult to scale, and hinder rapid interpretation of large genomic, proteomic, and imaging datasets.
Solution
Tacit Machines provides an integrated platform that connects laboratory data acquisition with GPU‑accelerated compute resources. The system ingests raw experimental data, automates preprocessing, and routes workloads to high‑performance clusters for parallel analysis. By leveraging GPU acceleration, the platform reduces processing times from days to hours, enabling faster hypothesis testing and iterative experimentation. Cloud‑native orchestration ensures that compute scales with data volume, while standardized APIs simplify integration with existing lab information management systems. The result is a streamlined end‑to‑end workflow that transforms traditional wet‑lab outputs into actionable insights at scale.
Target Audience
Primary customers are academic and industrial biology labs, genomics and proteomics core facilities, and biotech companies that need to accelerate large‑scale data analysis and integrate compute resources into their experimental workflows.
Features
- Automated data ingestion pipelines that capture sequencing, proteomics, and imaging outputs directly from lab instruments
- GPU‑optimized processing engines for common bioinformatics tasks such as alignment, variant calling, and image segmentation
- Cloud‑based orchestration layer that dynamically provisions compute resources based on workload demand
- Unified API and SDK for seamless integration with laboratory information management systems (LIMS) and custom analysis scripts
- Built‑in data provenance and versioning to track sample metadata and processing steps across the pipeline
- Scalable storage architecture that supports petabyte‑scale datasets with secure access controls