The Women AI Hackathon presents a unique opportunity for students to gain hands-on experience in the latest LLM technologies. Participate individually or as a team to build snap-ins integrating various APIs, creating algorithms for generative content, and utilizing AI technologies to enhance digital engagement.
<aside> 🧭 NAVIGATION
</aside>
<aside> ✅ Getting Started
</aside>
<aside> 🔗 Community Channel and Links
</aside>
| Start of the hackathon | 18th November 2024 (Monday) |
|---|---|
| Deadline for submissions | 27th November 2024 (Wednesday), 11:59 PM IST |
| Announcement of winners | By 4th December 2024 (Wednesday) |
We have 5 tracks for the hackathon. Participants can pick up any one out of the 5 problem statements, and build a snap-in that satisfies the use-case.
End-of-sprint reviews provide essential feedback but manually compiling these summaries can be time-consuming. Your challenge is to build a DevRev snap-in that automates this process, delivering a clear and actionable sprint summary to a Slack channel. The summary should capture key sprint highlights, including what went well, what went wrong, and any actionable insights for the team.
Detecting customer frustration in real-time can be challenging and often relies on manual observation, which may lead to delayed responses. Your task is to build a DevRev snap-in that analyzes customer sentiment continuously during conversations and triggers alerts for support teams when frustration is detected. This solution should enhance customer service by enabling timely intervention, potentially preventing escalations, and improving overall customer satisfaction.
Manual ticket tagging is time-consuming, inconsistent, and prone to errors. This can lead to delayed responses and misallocated resources. Your task is to build a snap-in that analyzes incoming tickets, automatically assigns them to the correct part of the product, and adds contextual tags to indicate the nature of the ticket (e.g., feature request, bug report) along with a brief explanation.
Support agents often lack immediate access to a customer's full context when handling new inquiries, leading to slower response times and potentially less satisfactory customer experiences. Your task is to build a snap-in that automatically collects and synthesizes customer information from various sources, presenting a concise summary and actionable insights to the agent at the start of each interaction.
Manually tracking and reporting closed deals can be inefficient and prone to errors. Your task is to build a DevRev snap-in that automates this process, generating a concise and useful summary that is automatically sent to a configurable Slack channel. This solution should help sales teams stay informed and make better decisions by providing actionable insights in real-time.