Simplified meeting setup through smart email parsing
Project
An AI-based Assistant Streamlining Meeting Organization At A High-end Staffing Provider
Client
Boldly
Multi-threaded email chains become concise overviews
Drastically reduced workload frees up hours every week
Client
Boldly
Boldly is a high-end, subscription-based staffing provider with a team based in North America and Europe. The company provides highly skilled remote employees to represent businesses and execute tasks responsibly and effectively.
Problem
Time-Consuming Meeting Organization
Boldly’s executive assistants were spending an inordinate amount of time organizing meetings for senior management. This resulted from a constant need to switch contexts while managing multiple meetings, which proved to be an all-consuming process.
Each assistant had to meticulously review lengthy correspondence in reverse order to stay abreast of the evolving status of each meeting. The labor-intensive task underscored the need to streamline meeting organization, reducing the adminsitrative overhead and increasing productivity.
Solution
AI Agent that Extracts Key Details from Emails
The DLabs.AI team harnessed the power of Large Language Models (LLMs) to address the challenge. We developed an AI-based virtual assistant capable of extracting crucial information from emails. It analyzes email content, presenting an immediate overview of the current status of a meeting by condensing lengthy, multi-threaded conversations into a concise update.
Key Features:
- Smart Information Extraction: The model extracts critical details from emails, including proposed meeting dates, locations, responses, even the food preferences of participants.
- Data Aggregation and Processing: The tool aggregates and processes the extracted data, presenting it in a clear, concise format.
- Real-Time Status Updates: Assistants receive up-to-date information on the current status of each meeting.
- Email Thread Summaries: The solution provides a bulleted history of email threads, ensuring a comprehensive understanding of a meeting’s status and enabling streamlined organization.
Please note: As this project is in development, we are not yet able to share specific results relating to its overall performance. Once deployed, we will update this case study with all relevant metrics.
Technologies used
Azure
GPT4
Langchain
MLFlow
Python
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