Maximize your profit margin with optimal pricing
Plutus is an intelligent pricing engine for any business, providing real-time, data-driven price recommendations for use cases across many industries.
With Plutus, organizations can maximize their profit margin and revenue potential by leveraging historical data to make better informed pricing decisions.
How Plutus Works
You can configure Plutus to generate price recommendations based on multiple factors, including inventory data, product catalog information, and purchasing history. Plutus can also factor in external market data such as competing product information and pricing.
Plutus generates price recommendations that you can either work with manually or plug in via an API directly into your e-commerce platform or retail product management system.
price based on
Seamless integration with
GTM price based on market intelligence
Seamless integration with any platform
A supply chain company was successful in automating its processes for on-demand production projection views by its global sales team with a goal of reducing cost and time spent to sort and analyze data. Trabeya leveraged Astrea to automatically clean data and added its deep learning capabilities to detect issues and alert users dynamically, to ensure they were free of errors.
This livestock business did not have real-time insights because its transactional systems weren’t optimized for analytics. Instead of building a data warehouse or a data lake, powered by data marts or cubes to serve individual analytics use cases, we were able to unify the semantics of their data. The ability to dynamically clean and detect errors enabled quality, error-free data, driving more meaningful insights quickly without the need to spend hours in sanitizing data. As a result, the company was able to reduce cost by more than 3x.
An organization that owns and operates a large cattle herd across multiple cities successfully moved a step ahead by enabling its recorded data was error-free.Even though it was successful in completely replacing its manual supply chain processes to record and maintain data, forecasts were not always accurate and required additional work by the team to clean and sanitize the available data for forecasting. This helped to increase weekly forecast accuracy by 25% initially due to the prevailing poor quality of data.
Trabeya used Astrea to combine checks for user-provided constraints for known business logic with unsupervised learning to perform tasks like imputation and error detection and correcting/fixing them to produce high-quality data sets. This data could then be validated easily and used for reporting, analytics, and modelling.
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