Sciloop Lab offers a cloud‑native AI scientist platform that automates the end‑to‑end machine‑learning research workflow. Researchers submit a code template and objectives, and the system generates hypotheses, provisions GPU/TPU resources, runs experiments with hyperparameter optimization, and compiles reproducible reports with metrics and code snippets. The platform provides version‑controlled logs, API integration, and enterprise security for academic and industry AI teams.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning researchers often allocate a large portion of their time to setting up compute environments, managing experiment pipelines, and manually documenting results, which slows iteration and hampers reproducibility.
Solution
Sciloop Lab provides a cloud‑native AI scientist agent that automates the full ML research lifecycle. Users submit an experiment template containing their codebase and research objective; the system then autonomously generates hypotheses, provisions cloud resources, runs and monitors experiments, and applies statistical analysis to identify improvements. Results are compiled into a structured research report with code snippets, figures, and methodological details. By handling infrastructure orchestration, hyperparameter optimization, and documentation, the platform lets researchers concentrate on conceptual innovation while maintaining traceable, reproducible workflows.
Target Audience
Primary users are academic and industry machine‑learning researchers, data‑science teams, and AI labs that conduct iterative model development and require reproducible experiment management.
Features
- Autonomous hypothesis generation and experiment design powered by large‑language‑model reasoning
- End‑to‑end cloud orchestration with automatic provisioning of GPU/TPU clusters and containerized environments
- Integrated hyperparameter search and model selection using Bayesian optimization and early‑stopping criteria
- Real‑time result aggregation, metric tracking, and statistical significance testing across experiment runs
- Automated drafting of research reports, including LaTeX‑compatible tables, plots, and code excerpts
- Version‑controlled experiment logs and reproducibility metadata exported to Git or S3 storage
- RESTful API and SDK for injecting custom codebases, data pipelines, or evaluation scripts
- Enterprise‑grade security with encrypted data transit, role‑based access controls, and audit trails