NABU

Intelligent Recommendation Engine
Increase customer satisfaction and revenue with AI-driven recommendations

NABU

Intelligent Recommendation Engine
Increase customer satisfaction and revenue with AI-driven recommendations

Nabu is a recommendation engine for any business, providing real-time, data-driven recommendations for use cases across e-commerce, agriculture, manufacturing & healthcare.

With Nabu organizations can maximize their revenue potential by leveraging historical data to make better informed decisions.

How Nabu Works

Nabu generates recommendations as an API output that plugs into your destination system, such as your e-commerce platform. Nabu is built on Trabeya Springboard as a plug-and-play application, leveraging the platform’s data connector library, unified data lake, and BI capabilities.

Nabu is a recommendation engine for any business, providing real-time, data-driven recommendations for use cases across e-commerce, agriculture, manufacturing & healthcare.

With Nabu organizations can maximize their revenue potential by leveraging historical data to make better informed decisions.

How Nabu Works

Nabu generates recommendations as an API output that plugs into your destination system, such as your e-commerce platform. Nabu is built on Trabeya Springboard as a plug-and-play application, leveraging the platform’s data connector library, unified data lake, and BI capabilities.

Nabu recommendation flow diagram

Value Propositions

Personalized
recommendations

Show personalized recommendations based on previous user interactions

Outcome-based
recommendations

Deliver recommendations for desired outcomes, such as maximization of profit, engagement, or reach

Contextually-aware
recommendations

Provide recommendations based on where the user is in the customer journey

Continuous learning
model

Like a fine wine, recommendations get better over time

Personalized
recommendations

Show personalized recommendations based on previous user interactions

Outcome-based recommendations

Deliver recommendations for desired outcomes, such as maximization of profit, engagement, or reach

Contextually-aware recommendations

Provide recommendations based on where the user is in the customer journey

Continuous learning model

Like a fine wine, recommendations get better over time

RECOMMENDATIONS FOR ANY INDUSTRY

Retail & E-commerce

Deliver personalized customer experiences and maximize revenue exposure

Learn More

Financial Services

Mitigate risk and increase revenue exposure by recommending financial products and services, such as loans and interest rates

Learn More

Healthcare

Deliver better patient outcomes and improve process efficiencies with AI-augmented personalized care.

Learn More

Agriculture

Optimize production and reduce wastage by empowering your staff with contextually-aware recommendations.

Learn More

Success Stories

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.

products

springboard
springboard
astrea
astrea
fides
fides
kassandra
kassandra
plutus
plutus
sia
sia

Let our team show how we can transform your business