Taqi Haider@Geeks

Chatty Panda is an artificial intelligence-powered tool that enables businesses to provide efficient and effective support to their customers. This chatbot is designed to handle customer queries related to business policies, documents, and other important information in multiple languages. The chatbot is programmed with a company-specific database of information related to business policies, regulations, and documents, which allows it to provide accurate and helpful responses to customer inquiries. With its multilingual capabilities, the chatbot can cater to customers from different parts of the world who speak various languages. Customers can interact with the chatbot through a user-friendly interface and ask questions related to their concerns about company policies. The chatbot uses natural language processing to understand the queries and provides relevant answers in real-time. The multilingual customer representative chatbot is a cost-effective solution for businesses looking to improve customer support and streamline their operations. It reduces the workload of customer service representatives and ensures that customers receive quick and accurate responses to their queries, ultimately leading to higher customer satisfaction and loyalty.

Our goal is to develop a system that can take documents in any language and allow users from different parts of the world with different languages to interact with those documents. This can help spread knowledge without any language barrier. We are first testing it out with Quranic text, which is in Arabic, and building a chatGPT-style bot for question answering. In future, we would like to expand it to additional documents in any language. We first perform embedding of the text using Cohere's multilingual embed model and save those embeddings in a vector database, Pinecone DB in our case. We then take user queries and also embed them and perform a similarity-based search to provide the most relevant results based on the query. The app is currently deployed and publicly available. Streamlit app link: https://taqihaider7-tafsir-quran-sementic-search-llm-app-fwh2if.streamlit.app/

Insur.Cap revolutionizes risk management with algorithmically driven augmented underwriting, leveraging computer vision AI & LAM for image-caption fusion. The orchestration processes proactively predict risks and facilitate accessible comprehensive coverage, overcoming traditional insurance limitations. Insur.Cap optimizes “Assistant-LAM” communication via a chatbot-based UI conversation flow interface. Looking from the perspective of Knowledge augmentation, we have a “data point issue” while the PROBLEM is that the incumbent does not employ DATA {as a tool, knowledge…} driven decision making, (to help processes make better-agile decisions by bringing in {data} more {usable} information to the risk underwriting, as a new data set - data points.) -Traditional insurance is too complex. -Definitely there is still a gap between the needs (mainly on-demand or custom-target needs). -Last but not least, proactive prevention might play a crucial role - if we emphasize prevention as a service proposition. Multimodal orchestration is our magic weapon! We develop a seamless-simple customer UuserInterface that delivers more/new datasets and data points for augmenting underwriting. Through the {Large Action/Agentic Model} we empower algorithmically driven architecture and orchestrate the process flow decision tree. Let me show you how we do that! First, with a simple User Interface we ingest the image and from the AI receive the CAPTION - this means the context from the image. That is the first pillar of the AI_assitant Then the core pillar of AI_ “Agent - Action” capability is to compute the: proper insurance product line based on the item from the caption execute premium calculation logic offer personalized coverage and finally issue the insurance policy All of that is our AI assistant Chatbot-based user interface; a SaaS (IaaS) API-driven technology stack.