Plumloom is a cloud‑based platform that lets AI research labs and enterprise ML teams create, run, and analyze evaluation suites for large language models. It provides a drag‑and‑drop interface, scalable execution on GPUs or external endpoints, and real‑time dashboards with metrics for accuracy, relevance, toxicity, fairness, and more, all with version control and privacy‑preserving data handling.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations developing or deploying large language models often lack standardized, scalable tools to assess model performance, safety, and bias across diverse tasks, leading to inconsistent evaluations and delayed improvements.
Solution
Plumloom offers a cloud‑based platform that centralizes the creation, execution, and analysis of LLM evaluation suites. Users can define custom test sets, integrate popular benchmark datasets, and run automated inference jobs on multiple model versions. The platform aggregates results into unified dashboards, providing quantitative metrics, error analyses, and visualizations that help teams compare models and track changes over time. Built-in support for privacy‑preserving data handling and API integrations enables seamless incorporation into existing MLOps pipelines.
Target Audience
Primary users are AI research labs, enterprise machine‑learning teams, and developers who need systematic evaluation of large language models before deployment.
Features
- Drag‑and‑drop interface for building evaluation pipelines without writing code
- Support for a wide range of evaluation types, including accuracy, relevance, toxicity, and fairness metrics
- Scalable execution engine that runs tests on hosted GPUs or connects to external inference endpoints
- Real‑time analytics dashboard with metric comparisons, trend charts, and per‑prompt error breakdowns
- Version control for datasets, prompts, and model configurations to ensure reproducibility
- API and SDKs for programmatic submission of evaluation jobs and retrieval of results