Fintech

A registered investment advisory firm with AUM of $140 million was spending 8 hrs to 24 hrs for processing block trades. This case-study explains how Menerva automated the process to bring the time down to under 30 mins.

Insights related to stock price drop to make stock purchase decisions

A registered investment advisory firm with AUM of $140 million was spending about 4 hrs on stock analysis. This case study explains how Menerva helped them bring the analysis time down to less than 2 secs.

A financial services company calculated performance scores for each of its business clients manually. This case study discusses how Menerva automated the process so the scores were computed simultaneously for all clients.

Retail

A Fortune 500 retailer with around 80 storefront locations in the US faced the challenge that all these locations had a legacy system which was incapable of identifying products sold or product correlations to assess top performing products in each of the stores. This case study explains how Menerva enabled the migration of the legacy system to a more modern and effective system.

A Fortune 50 Consumer Goods company wanted to implement a prototype to support functionality for an e-commerce use case that implemented a microservices based real-time streaming system using open source technologies. This case study explains how Menerva analyzed the use cases and implemented a microservices based solution that processed incoming data.

A food retail distributor in the business of distributing goods from multiple farms to multiple customers wanted some key business questions about revenue, profits and customer specific discounts addressed. This case study explains how Menerva analyzed their business data to provide insights which allowed the client to make critical business decisions.

Social Media

A commercial social media platform needed to implement an AI-driven recommendation engine that generated better or more relevant content for its users. This case study discusses how Menerva implemented an AI engine that automated the generation of recommendation periodically taking into account factors such as content recency, the frequency of access and other factors.