Kevin Vegda@kevin74
1
Events attended
1
Submissions made
United Kingdom
1 year of experience
About me
I’m an enthusiastic, forward-thinking AI Engineer who likes to keep up with the latest research and turn it into implementations. I have previous experience as a Product Engineer working on frontend, as well as a Machine Learning Engineer. I graduated summa cum laude with an MSc in Data Science and Analytics. I work well in interdisciplinary teams that require active collaboration and communication with varied stakeholders. My interests lie in Natural Language Processing, AI, and using them to automate the menial stuff.
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🤓 Latest Submissions
AI Contract Assistant
Artificial Intelligence and Generative AI have increasingly become the must-have technologies for businesses to increase revenue, reduce costs, and stay ahead of the competition. This is even truer for companies that rely on modern contract management technologies to manage their business agreements. Traditionally, contract management relied heavily on manual processes, with legal teams spending countless hours drafting, reviewing, and negotiating contracts. These methods, while effective, were often laborious, inefficient, and susceptible to human error. However, today, we bring you our AI Contract Assistant, a Generative AI Tool that can be used to negotiate contracts more efficiently, reducing time and money for enterprise sales teams, small business owners, startup entrepreneurs, or anyone who reads and signs a contract. We use the Upstage Document Parser API to extract text from PDFs and Word Documents, and we are using Llama 3.1 through TogetherAI to extract specific clause information from the extracted text. Clauses such as pricing information, term length, rights and exceptions, etc., can be extracted from lengthy contracts within seconds. We then send each clause into a specific AI Agent driven by CrewAI and to the RAG tool by Composio. Each AI Agent is trained on the specific contract language of that clause and gives a simplified analysis and recommendation for furthering the negotiation. The recommendation is based on an existing repository of standard contracts. On the front end served by Streamlit, the user can decide to Negotiate, Accept, or Reject each clause. If they choose to negotiate, they are asked to input their negotiation points. Using Llama 3.1, the system will draft a response email to the counterparty who provided the contract, indicating which clauses were accepted and rejected with recommendations and indicating which clauses have negotiation points to be negotiated further. Our current prototype is compatible with NDAs only.
16 Sep 2024
