Transforming Challenges into Opportunities: Our Success Stories

As a result of the Polyrific AI readiness assessment, Polyrific identified that the analytics our client relied on for profitability and forecasting were too segmented and independent. This became an immediate AI solution pursuit for our client.

Polyrific utilized AI tools to create a centralized and formalized analytics dashboard pulling in all the necessary metrics to yield automated and live reporting. Additionally, Polyrific implemented and trained a language model in which our client can interact with to query ad hoc data, as well as interrogate their data for further insights and/or comparison.

This process allowed users to save valuable time where instead of hunting down the right data they needed to use or verify, it could be queried and relayed to them nearly instantaneously. Combining this with the dashboard to aggregate their necessary metrics meant that no piece of data was ever too far, freeing up several analysts from tedious, monotonous tasks.

Upon completion of the AIRA, Polyrific suggested a need to develop and implement a sophisticated and automated data solution that would yield higher efficiencies and reliabilities across our client’s organization.

Polyrific architected a robust data ingestion, data cleansing, data mapping, data warehousing, and data output repository for our client. Within this framework, Polyrific engineered and trained a language model to analyze both structured and unstructured data. The language model would then organize the necessary data points into various weekly reports previously compiled by data analysts.

This freed up several analysts from the daily task of manually reviewing and computing hundreds of PDFs allowing them to focus their efforts on analytical and efficiency strategy. This LLM also reduced the risk of manual human-entry error for the organization.

Upon the completion of the AI readiness assessment, our client concluded that they had enough data to effectively set internal controls and KPIs to monitor not only customer policy benchmarks, but internal personnel efficiencies. They requested the use of a language model to comb through all their historical data and propose these benchmarks.

Polyrific trained a language model to effectively help our client set and govern internal and external KPIs to help boost their internal team’s efficiencies and productivity, as well as help look for revenue generation and upsell opportunities.

The use of the monitored KPI’s increased efficiencies in the client’s userbase by up to 40%, doing so with the ability to more easily generate revenue and leads by utilizing statistics that quickly and succinctly summarized the research previously done by hand combing through records. Going forward, record keeping and data management of the relevant data was also made much simpler with the use of the large language model vs. the previously manual process.

When migrating ERP’s, the client found there were issues that needed to be reconciled in their new data, as well as with the data all stored in Excel that was running the engine of their business. Decades of data was either still living in Excel, or moving from one ERP to the other, and Polyrific was able to facilitate that change.

Polyrific created a native web application that could accommodate all the manual processes such as calculations and pricing that was done previously using Excel workbooks and the previous ERP for reference. A relationship between the new ERP, the app, and a custom constructed backend database allowed for the entirety of the data that lived in Excel to be transitioned into their web app, which pulled its data automatically.

After completion of this project, the client no longer needed to rely on keeping old Excel files up to date with every minute change, as those were done in the ERP and automatically reflected in the app. Hundreds of man hours were saved over the course of just a few months of use, with some analysts and sales experts shaving off an entire week’s worth of time to be completed in just a couple hours each month.