LLM assistant for everyday tasks

Trustworthy, efficient, and secure LLMs deployment and finetuning to automate, speed-up, and improve day-to-day assignments

Starting Point

Identify a set of everyday tasks (organizing, reporting, translating, transcribing, responding, etc.) to be fully automated and accelerated.

Objective

Harness the power of (open-source) LLMs to automate the identified tasks in a trustworthy, efficient, and secure fashion.

Added Value​

Increase the productivity by automating routine tasks with LLMs and spend more time on challenging cases to improve the service quality.

From challenges to solutions

Routine tasks need time

Companies spend a lot of time on routine tasks such as preparing meeting minutes, replying to support queries, searching information, or translating documents. 

LLMs on the rise

LLMs express incredible abilities in natural language processing, understanding, and generating responses. The most of this functionality is immediately available on-demand and pay-as-you-go basis. However, specific know-how is required to integrate the LLMs with the existing IT.

Approach

We organize a short workshop presenting the basics on LLMs and AI assistants and identify those tasks in the enterprise everyday business which are feasible for cost-efficient automation. Based on the findings, we select, finetune, and deploy an appropriate LLM.

Methods

We already designed, implemented, and tested several patterns such as The “chatGPT” for your documents, automatic creation and visualization of frequent reports, converting meeting recordings into text, or automatically responding to customer inquiries and providing further information and options for action.

Impact

The quality of e.g. transcribed or translated documents is so high that very little post-processing is needed. This increases the productivity significantly and accelerates the processes: the meeting minutes are ready and available for all participants less than 10 minutes after the meeting.

Complex challenges

Enterprise-specific processes and policies require increased finetuning and LLM adaptation. For example, we automatically assignment incoming tickets to the predefined queues with more than 85% accuracy. For this purpose, we finetuned the LLM using a sample data set with 10% of the archived tickets originating from the previous year.

Interested in this topic?

Reach out to discuss suitable tasks for the customization and deployment of LLM assistant.