
Arthur AI
A platform that monitors and improves the performance of machine learning models.
USD | 2019 | 2020 | 2021 | 2023 |
---|---|---|---|---|
Revenues | 0000 | 0000 | 0000 | 0000 |
% growth | - | 110 % | 105 % | - |
EBITDA | 0000 | 0000 | 0000 | 0000 |
Profit | 0000 | 0000 | 0000 | 0000 |
EV | 0000 | 0000 | 0000 | 0000 |
EV / revenue | 00.0x | 00.0x | 00.0x | 00.0x |
EV / EBITDA | 00.0x | 00.0x | 00.0x | 00.0x |
R&D budget | 0000 | 0000 | 0000 | 0000 |
Source: Dealroom estimates
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Arthur.ai is a startup that provides a platform for deploying and managing machine learning models (MLMs) and large language models (LLMs). The platform is designed to be flexible and scalable, accommodating the dynamic needs of enterprises. It is model- and platform-agnostic, meaning it can work with any type of machine learning model and can be deployed on any platform, whether it's a leading cloud provider or an on-premise installation.
The company's main offering is a monitoring platform for MLMs and LLMs. This platform allows businesses to view all their models and manage their performance in one place, regardless of how they were built or where they are deployed. It also provides real-time metrics and optimization, alerting users when a metric crosses a set threshold.
Arthur.ai's solutions are designed to ensure that MLMs and LLMs adhere to rigorous standards and promote responsible practices. The company has strong connections with the research community, which helps it stay ahead of the curve in this fast-moving field.
The platform also facilitates collaboration and productivity by allowing for quick, seamless communication across teams and organizations. It features fully customizable and flexible permissions, enabling streamlined stakeholder engagement.
Arthur.ai operates in the machine learning operations (MLOps) market, serving clients that range from small businesses to large enterprises. Its business model is likely based on a subscription or usage-based pricing model, where clients pay for the services they use.
Keywords: Machine Learning Models, Large Language Models, Monitoring Platform, Real-time Metrics, Optimization, Collaboration, Productivity, Standards, Responsible Practices, MLOps.