Lunar streamlines the process of analysis, allowing you to focus on insights rather than technicalities.
Whether you are dealing with vast amounts of data or intricate workflows, Lunar offers a robust and reusable solution.
We outline some compelling arguments below for why Lunar is the best choice for optimizing your analytical workflows.
The fundamental premise behind Lunar is that the formula above leads to an increasing productivity in the construction of specialized agents leading to the expansion of our analytical frontier and collective rationality.
Moreover, when deploying it in real-world environments, users need to have the mechanism to support the coordination and orchestration of multiple generative AI expert systems, requiring the support of functionalities such as agent scheduling, logging, access management, etc.
Foundation models represent a step-change in AI, delivering the first, wide-applicable 'universal AI machines'. However, foundation models alone are limited in their applicability, in particular for highly specialized and complex analytical applications.
In order to address real-world problems (e.g. in scientific discovery, legal analysis and media creation), foundation models need to be combined with a diverse set of other functionalities in order to deliver Generative AI Expert Systems.
The fundamental premise behind Lunar is that the formula above leads to an increasing productivity in the construction of specialized agents leading to the expansion of our analytical frontier and collective rationality.
Moreover, when deploying it in real-world environments, users need to have the mechanism to support the coordination and orchestration of multiple generative AI expert systems, requiring the support of functionalities such as agent scheduling, logging, access management, etc.
Imagine that a Pharmaceutical company needs to build a generative AI expert agent that reliably extracts specific evidence from the scientific literature, automatically 'reading' a set of articles and extracting specific parameters into a table.
In order to deliver this specialized literature review agent, one is required to combine multiple functions, such as:
The fundamental premise behind Lunar is that the formula above leads to an increasing productivity in the construction of specialized agents leading to the expansion of our analytical frontier and collective rationality.
Moreover, when deploying it in real-world environments, users need to have the mechanism to support the coordination and orchestration of multiple generative AI expert systems, requiring the support of functionalities such as agent scheduling, logging, access management, etc.
Lunar is a framework which supports the agile development of Generative AI Expert Systems by combining:
The fundamental premise behind Lunar is that the formula above leads to an increasing productivity in the construction of specialized agents leading to the expansion of our analytical frontier and collective rationality.
Moreover, when deploying it in real-world environments, users need to have the mechanism to support the coordination and orchestration of multiple generative AI expert systems, requiring the support of functionalities such as agent scheduling, logging, access management, etc.
There are other low/no-code frameworks which have a similar look and feel to Lunar. However, many of these frameworks prioritize AI/software engineers and data scientists as end-users.
In contrast Lunar focuses on:
The fundamental premise behind Lunar is that the formula above leads to an increasing productivity in the construction of specialized agents leading to the expansion of our analytical frontier and collective rationality.
Moreover, when deploying it in real-world environments, users need to have the mechanism to support the coordination and orchestration of multiple generative AI expert systems, requiring the support of functionalities such as agent scheduling, logging, access management, etc.
