Browse applications built on BERT technology. Explore PoC and MVP applications created by our community and discover innovative use cases for BERT technology.
Navigating traditional bank websites can be an exasperating experience due to their complex menu structures. The Personalized Financial Advice Chatbot (PFAC) is designed to revolutionize this by using advanced natural language processing and machine learning. PFAC interprets user intents and preferences, streamlining information retrieval and enhancing the user experience. Instead of getting lost in the maze of menus, users can simply ask PFAC for account balances, loan advice, or any financial queries in plain language. PFAC aims to make online banking more user-centric, providing a stress-free, straightforward way to manage finances. This project delves into technical details, benefits, and the potential to transform online financial advice.
As organizations use Monday.com, they accumulate numerous documents, making it challenging to locate specific information. Members of the organization end up spending excessive time searching for documents on particular topics. Additionally, the difficulty in finding documents may lead to duplication because members may assume these documents don't exist but are actually buried within the existing volume. Enter Sortes, a AI agent organizational member equipped with comprehensive knowledge of all documents. Sortes swiftly provides summaries of topics already documented within the organization and directs members to the appropriate documents for further reading. Sortes achieves this extensive knowledge by leveraging AI models for passage ranking, context extraction, and content summarization. It creates a vector token index of all organization documents and quickly identifies relevant documents when queried, offering summaries and links to these pertinent resources.
Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)!Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be a podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)! This tool can take a YouTube transcript from one or more videos to be used to answer questions on a topic. The output will include a generated overall summary and generated key points from the video(s) by reading select parts of the transcript. The output will also include links to the relevant video, timestamped to the specific quote/snippet related to its respective key point. This tool can be useful to learners going through a video series playlist to review or identify where the series talks about a topic. It can also be used for educators in creating lessons from a series of videos. It also can be used for more casual enjoyment such as reviewing what the hosts have said on a particular topic. This use case is especially relevant for podcasts where hosts may revisit the same topic across multiple topics. Although Anthropic's Claude model can take in 100k tokens, this still creates a limit to what's read in by the LLM. This project will attempt to read in all the selected transcripts for the available model but if the transcript is too big for even the beefiest model, the tool will strategically select portions of the relevant transcripts based on the user fed question.
Recommendations cold-start problem is not actually a problem, if you leverage content and item metadata to build your recommendations. To showcase this idea we build a movie recommender, so you can visually see the difference between collaborative-filtering and content recommendations. We made two PRs to an existing open-source project Metarank: * support semantic recommendations with cohere-ai and sentence-transformers embeddings * use qdrant as a vector search engine to quickly perform vector similarity search With these two PRs merged building such a recommender is just a matter of a few lines of YAML code. But the semantic-similarity approach is not only about movies, but can be applied more generically in traditional places like e-commerce. For example, in fashion with high inventory churn, being able to recommend something for new clothes having zero feedback is really valuable.