Best AI tools for< Associate >

KeysAI is an AI-powered sales associate that helps dealerships save money and convert web traffic into dealership traffic. It is trained on cutting-edge automotive sales techniques and has a knowledge center that knows everything about your dealership, your inventory, and your customer. KeysAI is available 24/7 and can handle thousands of prospective buyers at the same time, for a fraction of the cost of traditional BDCs. It converts web traffic into foot traffic at your dealership, which means you sell more cars. KeysAI drives more leads at lower costs than your current chat solution and increases your ROI.

Mako AI is an AI-powered associate designed to revolutionize the workflows of investment firms by streamlining research, analysis, and drafting processes. It offers essential tools to simplify data access, safeguard information, and provide actionable insights. With features like enterprise search, chat capabilities, and a knowledge base, Mako AI centralizes institutional knowledge and ensures data security with SOC 2 Type II certification. The application is easy to implement, prioritizes security, and enhances collaboration within firms.

Prompt Security is a platform that secures all uses of Generative AI in the organization: from tools used by your employees to your customer-facing apps.

integrate.ai is a platform that enables data and analytics providers to collaborate easily with enterprise data science teams without moving data. Powered by federated learning technology, the platform allows for efficient proof of concepts, data experimentation, infrastructure agnostic evaluations, collaborative data evaluations, and data governance controls. It supports various data science jobs such as match rate analysis, exploratory data analysis, correlation analysis, model performance analysis, feature importance & data influence, and model validation. The platform integrates with popular data science tools like Azure, Jupyter, Databricks, AWS, GCP, Snowflake, Pandas, PyTorch, MLflow, and scikit-learn.

ThankYouNote.app is an AI-powered tool that helps users write personalized and heartfelt thank-you notes for any occasion. It offers a range of templates and examples to choose from, making it easy to express gratitude in a thoughtful and meaningful way. The tool is designed to assist users in crafting custom thank-you notes that are perfect for any situation, whether it's for a gift, an act of kindness, or simply to show appreciation.

Just Walk Out technology is a checkout-free shopping experience that allows customers to enter a store, grab whatever they want, and quickly get back to their day, without having to wait in a checkout line or stop at a cashier. The technology uses camera vision and sensor fusion, or RFID technology which allows them to simply walk away with their items. Just Walk Out technology is designed to increase revenue with cost-optimized technology, maximize space productivity, increase throughput, optimize operational costs, and improve shopper loyalty.

Sensei AI is a real-time interview copilot application designed to provide assistance during live interviews. It offers instant answers to questions, personalized responses, and aims to help users land their dream job. The application uses advanced AI insights to understand the true intent behind interview questions, tailoring responses based on tone, word choices, keywords, timing, formality level, and context. Sensei AI also offers a hands-free experience, robust privacy features, and a personalized interview experience by tailoring answers to the user's job role, resume, and personal stories.

Intuitivo is an AI/Computer Vision company building the future of retail, designing the perfect one-on-one shopping experience. We aim to create a connected, physical point of contact by meeting your client halfway; no lines, no friction. Our A-POPs facilitate seamless, cash-free purchases that naturally incorporate themselves into any customer’s routine. It’s simple, fully automated, and digitally intuitive.

Caper is an AI-powered smart shopping cart technology that revolutionizes the in-store shopping experience for retailers. It offers seamless and personalized shopping, incremental consumer spend, and alternate revenue streams through personalized advertising and loyalty program integration. Caper enhances engagement with digital coupons, prevents shrink, and provides real-time contextual advertising. The smart cart features a captivating screen, personalization, advertising opportunities, gamification, and loyalty program utilization. It integrates with existing POS systems, offers anti-theft capabilities, and provides advanced analytics for operational efficiency. Caper is transforming the retail landscape by unifying online and in-store shopping for the best grocery experience.

Maigon is a state-of-the-art AI application designed for contract review. It offers efficiency in closing deals fast by providing AI-driven contract review tools that screen agreements, answer legal questions, and offer guidance for finalizing contracts in record time. Maigon integrates the latest deep learning technology and supports various contract types based on customer demand. The platform also announces the integration of OpenAI's GPT-4 for enhanced compliance review experience.

Boutiq is an AI-powered video clienteling platform that helps Shopify stores provide a more personalized and engaging shopping experience for their customers. With Boutiq, customers can start or schedule a video chat with a sales associate anywhere on the Shopify store, allowing them to get personalized advice and assistance without leaving their home. This can lead to higher sales, increased customer satisfaction, and reduced returns.

Blozum is an advanced AI chat assistant application designed to enhance website conversions. It offers digital sales assistants that act as 24/7 sales force, engaging with platform visitors, providing instant answers, and guiding customers through purchase journeys. The application leverages AI to optimize interactions, personalize user experiences, and streamline the sales process. Blozum is suitable for various industries such as Ecommerce, Real Estate, Retail, Web3, Insurance, Banking, Edtech, Healthcare, and more.

Beacon Biosignals provides an EEG neurobiomarker platform that is designed to accelerate clinical trials and enable new treatments for patients with neurological and psychiatric diseases. Their platform is powered by machine learning and a world-class clinico-EEG database, which allows them to analyze existing EEG data for insights into mechanisms, PK/PD, and patient stratification. This information can be used to guide further development efforts, optimize clinical trials, and enhance understanding of treatment efficacy.

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

Nara is an AI-powered digital sales associate that helps online stores increase sales and provide 24/7 support across all chat channels. It automates customer engagement by answering support questions, providing tailored shopping advice, and simplifying customer checkout. Nara offers different pricing plans to suit varying needs and provides a human touch experience similar to interacting with a helpful sales associate in physical stores.

Krisp is an AI-powered tool designed to enhance online meetings and calls by removing background noises, transcribing conversations in real-time, generating meeting notes and summaries, and providing features like AI accent localization and call recording. It offers solutions for individuals, teams, call centers, and enterprises, ensuring clear communication and improved productivity. Krisp's advanced technology helps users focus on the conversation without distractions and ensures high-quality audio interactions.

Ringover is an AI-driven conversation platform designed for staffing and sales teams. It offers features such as transcription and call summaries, mood analysis, cloud telephony, multichannel communications, sales prospecting automations, app marketplace integration, and more. The platform aims to centralize all communication channels within a simple interface, empowering users to enhance productivity and streamline conversations with clients and prospects. Ringover also provides advanced analytics, automation, and coaching to boost the productivity of recruiting and sales teams. With seamless integration with various business tools, Ringover offers a comprehensive solution for businesses looking to optimize their communication strategies.

Glean is an AI-powered work assistant and enterprise search platform that enables teams to harness generative AI to make better decisions faster. It connects all company data, provides advanced personalization, and ensures retrieval of the most relevant information. Glean offers responsible AI solutions that scale to businesses, respecting permissions and providing secure, private, and fully referenceable answers. With turnkey deployment and a variety of platform tools, Glean helps teams move faster and be more productive.

Chattr is an automated AI hiring software designed for frontline industries such as restaurants, retail, senior living, and labor. It offers solutions for attracting, preparing, and syncing the hiring process through conversational AI, onboarding automation, and integrations with various systems. Chattr streamlines recruiting, onboarding, and candidate relationship management, making frontline hiring efficient and cost-effective.

Cactus Communications is a science communication and technology company specializing in AI products and solutions for research funding, publication, communication, and discovery. Their services include editorial services, author education, research promotion, technology solutions, and medical communications.

The LLM Hosting Container repository provides Dockerfile and associated resources for building and hosting containers for large language models, specifically the HuggingFace Text Generation Inference (TGI) container. This tool allows users to easily deploy and manage large language models in a containerized environment, enabling efficient inference and deployment of language-based applications.

ai.robots.txt is an open list of web crawlers associated with AI companies and the training of LLMs to block. Users are encouraged to contribute to and implement this list on their own site. The list includes crawlers sourced from Dark Visitors, and contributors can add information about a crawler by making a pull request with the bot name added to `robots.txt`, `ai.txt`, and any relevant details in `table-of-bot-metrics.md`.

aiohttp-devtools provides dev tools for developing applications with aiohttp and associated libraries. It includes CLI commands for running a local server with live reloading and serving static files. The tools aim to simplify the development process by automating tasks such as setting up a new application and managing dependencies. Developers can easily create and run aiohttp applications, manage static files, and utilize live reloading for efficient development.

Large Language Models (LLM) are used to browse the Wolfram directory and associated URLs to create the category structure and good word embeddings. The goal is to generate enriched prompts for GPT, Wikipedia, Arxiv, Google Scholar, Stack Exchange, or Google search. The focus is on one subdirectory: Probability & Statistics. Documentation is in the project textbook `Projects4.pdf`, which is available in the folder. It is recommended to download the document and browse your local copy with Chrome, Edge, or other viewers. Unlike on GitHub, you will be able to click on all the links and follow the internal navigation features. Look for projects related to NLP and LLM / xLLM. The best starting point is project 7.2.2, which is the core project on this topic, with references to all satellite projects. The project textbook (with solutions to all projects) is the core document needed to participate in the free course (deep tech dive) called **GenAI Fellowship**. For details about the fellowship, follow the link provided. An uncompressed version of `crawl_final_stats.txt.gz` is available on Google drive, which contains all the crawled data needed as input to the Python scripts in the XLLM5 and XLLM6 folders.

The MoonshotAI-Cookbook provides example code and guides for accomplishing common tasks with the MoonshotAI API. To run these examples, you'll need an MoonshotAI account and associated API key. Most code examples are written in Python, though the concepts can be applied in any language.

OpenCRISPR is a set of free and open gene editing systems designed by Profluent Bio. The OpenCRISPR-1 protein maintains the prototypical architecture of a Type II Cas9 nuclease but is hundreds of mutations away from SpCas9 or any other known natural CRISPR-associated protein. You can view OpenCRISPR-1 as a drop-in replacement for many protocols that need a cas9-like protein with an NGG PAM and you can even use it with canonical SpCas9 gRNAs. OpenCRISPR-1 can be fused in a deactivated or nickase format for next generation gene editing techniques like base, prime, or epigenome editing.

This repository showcases how to build a simple LLM-based chatbot for answering questions based on documents using retrieval augmented generation (RAG) technique. It also provides guidance on deploying the chatbot using Podman or on the OpenShift Container Platform. The workshop associated with this repository introduces participants to LLMs & RAG concepts and demonstrates how to customize the chatbot for specific purposes. The software stack relies on open-source tools like streamlit, LlamaIndex, and local open LLMs via Ollama, making it accessible for GPU-constrained environments.

AI Goat is a tool designed to help users learn about AI security through a series of vulnerable LLM CTF challenges. It allows users to run everything locally on their system without the need for sign-ups or cloud fees. The tool focuses on exploring security risks associated with large language models (LLMs) like ChatGPT, providing practical experience for security researchers to understand vulnerabilities and exploitation techniques. AI Goat uses the Vicuna LLM, derived from Meta's LLaMA and ChatGPT's response data, to create challenges that involve prompt injections, insecure output handling, and other LLM security threats. The tool also includes a prebuilt Docker image, ai-base, containing all necessary libraries to run the LLM and challenges, along with an optional CTFd container for challenge management and flag submission.

Voidpulse is an open-source Mixpanel alternative with AI capabilities. It is currently in private beta and being used in production for the Voidpet app. The project aims to provide analytics functionalities without the high cost associated with other tools. It is built using React, Typescript, Next.js on the frontend, and Node.js with TRPC & Drizzle ORM on the backend. Data is stored in Postgresql, Clickhouse is used for storing/querying events, Kafka for batch event insertion, and Redis for caching.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

Midjourney-proxy is a proxy for the Discord channel of MidJourney, enabling API-based calls for AI drawing. It supports Imagine instructions, adding image base64 as a placeholder, Blend and Describe commands, real-time progress tracking, Chinese prompt translation, prompt sensitive word pre-detection, user-token connection to WSS, multi-account configuration, and more. For more advanced features, consider using midjourney-proxy-plus, which includes Shorten, focus shifting, image zooming, local redrawing, nearly all associated button actions, Remix mode, seed value retrieval, account pool persistence, dynamic maintenance, /info and /settings retrieval, account settings configuration, Niji bot robot, InsightFace face replacement robot, and an embedded management dashboard.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

DeepSeek is a new experimental architecture for a large language model (LLM) powered internet-scale retrieval engine. Unlike current research agents designed as answer engines, DeepSeek aims to process a vast amount of sources to collect a comprehensive list of entities and enrich them with additional relevant data. The end result is a table with retrieved entities and enriched columns, providing a comprehensive overview of the topic. DeepSeek utilizes both standard keyword search and neural search to find relevant content, and employs an LLM to extract specific entities and their associated contents. It also includes a smaller answer agent to enrich the retrieved data, ensuring thoroughness. DeepSeek has the potential to revolutionize research and information gathering by providing a comprehensive and structured way to access information from the vastness of the internet.

This repository is designed to inject backdoors into Language Model Models (LLMs) for code. The injected backdoors serve as timestamps for the training dataset of the LLMs. The code is randomly generated and includes watermark backdoors to show specific behaviors. A script automatically updates the repository with a new backdoor every month. Validating the existence of the backdoor can infer when the training dataset was collected. The backdoors are constructed in a specific format, and verifying them may require multiple tries. The repository keeps a record of backdoors injected along with associated dates.

CLadder is a repository containing the CLadder dataset for evaluating causal reasoning in language models. The dataset consists of yes/no questions in natural language that require statistical and causal inference to answer. It includes fields such as question_id, given_info, question, answer, reasoning, and metadata like query_type and rung. The dataset also provides prompts for evaluating language models and example questions with associated reasoning steps. Additionally, it offers dataset statistics, data variants, and code setup instructions for using the repository.

This repository provides a ready-to-run basic Slack AI solution that allows users to summarize threads and channels using OpenAI. Users can generate thread summaries, channel overviews, channel summaries since a specific time, and full channel summaries. The tool is powered by GPT-3.5-Turbo and an ensemble of NLP models. It requires Python 3.8 or higher, an OpenAI API key, Slack App with associated API tokens, Poetry package manager, and ngrok for local development. Users can customize channel and thread summaries, run tests with coverage using pytest, and contribute to the project for future enhancements.

Sarathi-Serve is the official OSDI'24 artifact submission for paper #444, focusing on 'Taming Throughput-Latency Tradeoff in LLM Inference'. It is a research prototype built on top of CUDA 12.1, designed to optimize throughput-latency tradeoff in Large Language Models (LLM) inference. The tool provides a Python environment for users to install and reproduce results from the associated experiments. Users can refer to specific folders for individual figures and are encouraged to cite the paper if they use the tool in their work.

TriForce is a training-free tool designed to accelerate long sequence generation. It supports long-context Llama models and offers both on-chip and offloading capabilities. Users can achieve a 2.2x speedup on a single A100 GPU. TriForce also provides options for offloading with tensor parallelism or without it, catering to different hardware configurations. The tool includes a baseline for comparison and is optimized for performance on RTX 4090 GPUs. Users can cite the associated paper if they find TriForce useful for their projects.

A gpt assistant that helps with analyzing a startup/market. The answers you get back is already structured to give you the core elements you would want to see in an investment memo/ market analysis

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This AI will provide insights into the psychology and symbolism associated with colors.

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