2024-05-25 17:50:28

By now, you’ve probably heard about large language models, or LLMs—tools like ChatGPT that can understand, interpret, and generate human language with a high degree of contextual awareness and accuracy. They are trained on expansive datasets to perform a wide array of language-based tasks, ranging from answering questions and summarizing documents to generating original content. You may have even tried using one or more of these tools to answer questions, write emails, or come up with ideas for a new blog article.

While these well-known use cases for LLMs can save valuable time and energy for individuals, they are only scratching the surface of how LLMs can improve efficiency at the organizational level. With their adeptness at understanding and generating human language, LLMs can go beyond content generation to manage a myriad of tasks, right from operations and technical process automations to enterprise search, analytics, sales and marketing, and beyond .

In a business setting, LLMs can be invaluable for to solving operational inefficiencies at scale. They can automate those routine tasks that typically consume a considerable amount of your team’s resources and attention.

Here at Free Range, we’re excited to be working on a number of ongoing LLM projects that are radically improving operational efficiency for our clients. Let’s take a look under the hood.

Reducing 90% of the time required to perform manual tasks for a Continuing Medical Education (CME) Client

We are currently working with a Continuing Medical Education (CME) client to reduce their need for manual labor in their workflow. The client owns a management platform for administrators and clinicians that helps automate the process of managing medical licenses and certifications. From state licenses and board certifications to other expiring credentials, the platform keeps processes moving efficiently with a set of reminders and checklists.

Initially, the CME client had a team manually going through certificates and transcripts, checking expiration dates, degrees, and state rules, and then creating a checklist for the physician to renew their certificates and transcripts. While this information was invaluable for physicians, the process of manually collecting it created bottlenecks at scale.

We partnered with this client to create an internal product called RulesEngine. The purpose of RulesEngine was to parse the certificates and transcripts of physicians and create a checklist of things they would need to do to keep their current degrees relevant.

Physicians could then follow this task list and upload the new course certificates to complete their checklist automatically. These course completion certificates were then organized to be used to apply for their license renewal.

RulesEngine used LLMs and OCR tools to automate this entire process of parsing documents, parsing relevant emails and completing the required tasks without any manual intervention of the team. The team only reviewed all the tasks at the end rather than completing the repetitive data-collecting tasks themselves.

Powered by LLMs, RulesEngine has reduced time to perform manual operations to less than 90% and helped our client optimize their time and effort as they scale.

Data Analytics Illustrations

Improving feedback loops and inter-team communication in a large manufacturing client

We were approached by a water slide manufacturing organization of 300 people. Their owner had read about Free Range’s operational efficiency program and wanted to automate internal operations. To operate more efficiently, the company needed to address an ongoing communication problem.

Specifically, the company’s internal teams worked in silos, with very little inter-team communication happening between their various business heads. While management did meet every two weeks to discuss operations, critical decision-making frequently got lost in the shuffle during the long stretches of time between meetings, and teams were often in the dark about what was going on in the rest of the organization.

For example, marketing did not know what the company’s production capacity was. Meanwhile, the production head didn't know where sales was focused, and the inventory head was surprised to learn about shortages that manufacturing faced on a near-daily basis. 

Luckily, all valuable metrics from sales, marketing, production, inventory, and billing were being tracked in a large database of structured data—but the information was siloed and difficult to access. 

To help solve this problem and improve communication between departments, we partnered with the client to build an internal product called AskMe&Confirm.

AskMe&Confirm is a simple interface that can quickly answer questions posed by management heads in plain language. Using LLMs, AskMe&Confirm converts users’ questions into database queries that are then fired to retrieve valuable information from the database. The product then prompts users to confirm the retrieved data with the appropriate department heads to increase validation. 

This tool will allow key stakeholders to get visibility into the business processes, ask the right questions, and get meaningful insights—thus accelerating the erstwhile long feedback loops.