Best AI tools for< Rank Applicants >

Businessflow AI is a recruitment software powered by AI agents that helps companies find the best-fit applicants at a higher speed and reduced cost. It offers features such as resume analysis, applicant ranking, AI-powered interviews, and a general assistant. Businessflow AI aims to revolutionize the recruitment process by leveraging AI to automate tasks and provide data-driven insights.

Endorsed is an AI-powered recruiting tool that helps companies review job applications more efficiently and effectively. It integrates with your existing applicant tracking system (ATS) to provide you with a co-pilot that can help you screen and rank candidates, identify top talent, and make better hiring decisions. Endorsed uses machine learning to analyze candidate resumes and applications, and then ranks them based on your specific criteria. This helps you to identify the most qualified candidates quickly and easily, so you can spend less time reviewing applications and more time interviewing the best candidates.

Swiftly AI is an AI-powered recruiting tool designed to streamline the candidate selection process for businesses. By leveraging artificial intelligence, Swiftly AI helps recruiters quickly identify and rank qualified candidates based on specified role requirements. The platform enables users to efficiently filter through a large pool of applicants, saving time and resources in the recruitment process. With Swiftly AI, companies can accelerate their talent search and make data-driven hiring decisions to recruit the most suitable candidates at scale.

Arya by Leoforce is an AI recruitment software powered by ML technology. It offers advanced AI capabilities to source, screen, rank, and engage high-quality candidates faster. Arya empowers recruiters to focus on building meaningful candidate connections by going beyond simple keyword matching. It provides comprehensive talent sourcing and engagement capabilities, driving measurable improvements to candidate quality and reducing time to submit. Arya also enhances engagement and the candidate experience by providing access to new and updated candidate information daily, a 24/7 AI recruiting assistant, and a centralized multi-channel communication dashboard.

ZapHire is an AI-powered recruitment tool that helps companies find the perfect fit for their teams by ranking, categorizing, and curating candidates based on their skills, experience, and expertise. The tool conducts comprehensive background checks on candidates using data from top channels like Github, Stackoverflow, Twitter, LinkedIn, and ProductHunt. With over 40 million indexed accounts and specialized algorithms, ZapHire provides accurate scores and ranks to help companies confidently choose the best candidate for the job. It offers a large pool of highly qualified candidates, reduces time-to-fill positions, improves candidate quality, and provides cost-effective sourcing. The tool centralizes all channels and social profiles, provides regularly updated insights, and ranks and categorizes profiles to show the most important information.

Arya by Leoforce is an AI recruitment software powered by ML technology. It offers AI recruiting automation, integrations with applicant tracking systems, multi-channel candidate sourcing, resume search, direct sourcing, talent intelligence, talent matching, candidate ranking, diversity recruiting, candidate experience, and engagement. Arya assists in high volume hiring needs, specialized hiring needs, and provides solutions tailored to various industries such as staffing, technology, healthcare, logistics, banking, and finance. It is designed to enhance the recruitment process by automating and optimizing activities, saving time and improving hiring outcomes.

Rank One Computing (ROC) is an American-made provider of multimodal biometrics and computer vision solutions, specializing in face recognition, fingerprint recognition, and artificial intelligence technologies. Trusted by the U.S. military, law enforcement, and leading FinTech brands, ROC offers top-ranked software for identity proofing and threat detection. Their suite of products includes ROC SDK, ROC Watch, and custom enterprise AI development services. With a focus on security and efficiency, ROC aims to make the world safer and more convenient through unbiased and privacy-conscious applications.

Google Business Profile Growth Manager is an application that helps businesses manage their Google Business Profile and improve their local SEO. It offers a variety of features to help businesses save time and grow their online presence, including the ability to manage multiple locations, schedule posts, monitor growth, and get AI-powered insights.

FluidSEO is an AI-infused Webflow SEO application that helps users fix SEO problems efficiently. It offers features such as smart alt text generation, schema creation, bulk updates, and smart descriptions. The application streamlines the process of adding metadata and ensuring alt text for images, saving users time and effort. With FluidSEO, users can implement best practice SEO in Webflow with confidence, improve their site's ranking on Google, and simplify on-page SEO tasks. The application is designed to be user-friendly, making it suitable for Webflow designers, SEO managers, content marketers, and beginners.

PostPulse is an AI-powered application designed to elevate the online presence and SEO performance of SaaS startups. It generates high-quality, SEO-optimized content and publishes it on premium domains to improve search engine rankings and drive organic traffic. With flexible subscription plans and features like auto-scheduling, PostPulse aims to help SaaS founders enhance their digital footprint effortlessly.

Candydate is a video recruitment platform that leverages artificial intelligence to streamline the hiring process. It allows employers to assess candidates through short videos, analyzing speech, body language, and personal traits to determine the right personality-fit for any job. With Candydate, hiring becomes more efficient and informed, enabling businesses to make better decisions and fill vacancies quickly.

Elavra is an AI Image Gallery application that utilizes artificial intelligence technology to organize and display images based on user preferences. Users can explore a wide range of images, including models, animals, art, and more. The platform offers features such as image reactions, favorites, and user rankings. Elavra aims to provide a visually engaging experience for users to discover and share images in a user-friendly interface.

Junia AI is a leading AI writer tool designed for SEO and content generation. It offers a comprehensive solution for creating high-quality, SEO-optimized, and ready-to-rank content in minutes. With features like AI writer, auto-generation of images, auto SEO research, and long-form content creation, Junia AI streamlines the content creation process. Users can elevate their site's SEO, drive organic traffic, and improve visibility with the help of this AI application. Junia AI stands out with its ability to generate people-first content, tackle keyword research, optimize meta data, and enhance content quality. It is a game-changer for bloggers, copywriters, and businesses looking to enhance their content creation workflow with AI technology.

Rank Math is an AI-powered SEO tool that helps you optimize your website for search engines. It offers a variety of features to help you improve your website's ranking, including keyword research, on-page optimization, and link building. Rank Math also provides detailed analytics to help you track your progress and identify areas for improvement.

AI Top Rank is an AI tool designed to gather feedback and feature requests from users. It provides a platform for users to share their ideas, suggestions, and feedback on various topics. The tool helps in organizing and prioritizing feedback to improve products or services. With AI capabilities, it offers intelligent insights and analysis to drive decision-making processes. AI Top Rank aims to enhance user engagement and satisfaction by incorporating user feedback into product development.

AlphaRank is an AI SEO optimizer tool designed to boost website traffic by providing advanced SEO solutions. It offers a comprehensive suite of features to audit and optimize websites, leveraging AI algorithms and a vast database of keywords and merchant insights. Trusted by top-tier partners, AlphaRank aims to enhance content relevance, user engagement, and SEO precision for businesses, bloggers, and freelancers. The tool streamlines SEO operations, provides market insights, and offers expert recommendations for technical and content improvements.

CTRify is an AI-powered SEO action platform that helps businesses create websites that rank on Google. By providing a single keyword to the platform's artificial intelligence, businesses can boost their SERP rankings, organic CTR, dwell time, and pogo sticking. CTRify also provides organic traffic from millions of real desktop and mobile devices connected from residential IPs of top mobile carriers from all locations of the world.

CanIRank is an AI-powered SEO software designed for small businesses and startups to improve their Google rankings. It provides specific action recommendations based on AI analysis, offers DIY instructions or the option to delegate to an SEO consultant. CanIRank helps users understand why pages rank where they do, delivers results by suggesting impactful changes, and keeps users informed about new content ideas and ranking changes. The software automates keyword research, provides insights on competitive keywords, and offers a Keyword ROI Score to find the best balance between ranking difficulty and keyword value. CanIRank also offers SERP analysis, rank tracking, and competitive analysis tools to help users optimize their SEO strategies.

SEOByAI is a website that offers a suite of free AI-powered SEO tools to help businesses improve their search engine rankings. The tools include an AI blog featured image generator, an AI content detector, an AI outline generator, an AI title generator, an AI meta description generator, an AI SEO topic cluster generator, an AI paragraph generator, and an AI paragraph rewriter. The website also features a blog with articles on SEO best practices and a newsletter that provides updates on the latest SEO trends.

Autoblogging.ai is an AI-powered article writer that helps users generate high-quality, SEO-optimized content in one click. It offers multiple modes, including Quick Mode for fast article generation, Pro Mode for customization, and Godlike Mode for keyword-rich content. Autoblogging.ai supports multiple languages and provides features like bulk generation, automatic WordPress posting, and Amazon review generation. It is designed to save time and improve online visibility for bloggers, website owners, and agencies.

AutoArena is a tool designed to create leaderboards ranking Language Model outputs against one another using automated judge evaluation. It allows users to rank outputs from different LLMs, RAG setups, and prompts to find the best configuration of their system. Users can perform automated head-to-head evaluation using judges from various platforms like OpenAI, Anthropic, and Cohere. Additionally, users can define and run custom judges, connect to internal services, or implement bespoke logic. AutoArena enables users to run the application locally, providing full control over their environment and data.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

This repository contains executable Python notebooks, sample apps, and resources for testing out the Elastic platform. Users can learn how to use Elasticsearch as a vector database for storing embeddings, build use cases like retrieval augmented generation (RAG), summarization, and question answering (QA), and test Elastic's leading-edge capabilities like the Elastic Learned Sparse Encoder and reciprocal rank fusion (RRF). It also allows integration with projects like OpenAI, Hugging Face, and LangChain to power LLM-powered applications. The repository enables modern search experiences powered by AI/ML.

This repository contains course materials for T81 559: Applications of Generative Artificial Intelligence at Washington University in St. Louis. The course covers practical applications of Large Language Models (LLMs) and text-to-image networks using Python. Students learn about generative AI principles, LangChain, Retrieval-Augmented Generation (RAG) model, image generation techniques, fine-tuning neural networks, and prompt engineering. Ideal for students, researchers, and professionals in computer science, the course offers a transformative learning experience in the realm of Generative AI.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see Milvus Architecture Overview. Milvus was released under the open-source Apache License 2.0 in October 2019. It is currently a graduate project under LF AI & Data Foundation.

MSCCL++ is a GPU-driven communication stack for scalable AI applications. It provides a highly efficient and customizable communication stack for distributed GPU applications. MSCCL++ redefines inter-GPU communication interfaces, delivering a highly efficient and customizable communication stack for distributed GPU applications. Its design is specifically tailored to accommodate diverse performance optimization scenarios often encountered in state-of-the-art AI applications. MSCCL++ provides communication abstractions at the lowest level close to hardware and at the highest level close to application API. The lowest level of abstraction is ultra light weight which enables a user to implement logics of data movement for a collective operation such as AllReduce inside a GPU kernel extremely efficiently without worrying about memory ordering of different ops. The modularity of MSCCL++ enables a user to construct the building blocks of MSCCL++ in a high level abstraction in Python and feed them to a CUDA kernel in order to facilitate the user's productivity. MSCCL++ provides fine-grained synchronous and asynchronous 0-copy 1-sided abstracts for communication primitives such as `put()`, `get()`, `signal()`, `flush()`, and `wait()`. The 1-sided abstractions allows a user to asynchronously `put()` their data on the remote GPU as soon as it is ready without requiring the remote side to issue any receive instruction. This enables users to easily implement flexible communication logics, such as overlapping communication with computation, or implementing customized collective communication algorithms without worrying about potential deadlocks. Additionally, the 0-copy capability enables MSCCL++ to directly transfer data between user's buffers without using intermediate internal buffers which saves GPU bandwidth and memory capacity. MSCCL++ provides consistent abstractions regardless of the location of the remote GPU (either on the local node or on a remote node) or the underlying link (either NVLink/xGMI or InfiniBand). This simplifies the code for inter-GPU communication, which is often complex due to memory ordering of GPU/CPU read/writes and therefore, is error-prone.

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

This practical free hands-on course focuses on Large Language models and their applications, providing a hands-on experience using models from OpenAI and the Hugging Face library. The course is divided into three major sections: Techniques and Libraries, Projects, and Enterprise Solutions. It covers topics such as Chatbots, Code Generation, Vector databases, LangChain, Fine Tuning, PEFT Fine Tuning, Soft Prompt tuning, LoRA, QLoRA, Evaluate Models, Knowledge Distillation, and more. Each section contains chapters with lessons supported by notebooks and articles. The course aims to help users build projects and explore enterprise solutions using Large Language Models.

R2R (RAG to Riches) is a fast and efficient framework for serving high-quality Retrieval-Augmented Generation (RAG) to end users. The framework is designed with customizable pipelines and a feature-rich FastAPI implementation, enabling developers to quickly deploy and scale RAG-based applications. R2R was conceived to bridge the gap between local LLM experimentation and scalable production solutions. **R2R is to LangChain/LlamaIndex what NextJS is to React**. A JavaScript client for R2R deployments can be found here. ### Key Features * **🚀 Deploy** : Instantly launch production-ready RAG pipelines with streaming capabilities. * **🧩 Customize** : Tailor your pipeline with intuitive configuration files. * **🔌 Extend** : Enhance your pipeline with custom code integrations. * **⚖️ Autoscale** : Scale your pipeline effortlessly in the cloud using SciPhi. * **🤖 OSS** : Benefit from a framework developed by the open-source community, designed to simplify RAG deployment.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

LangChain4j-AIDeepin is an open-source, offline deployable retrieval enhancement generation (RAG) project based on large language models such as ChatGPT and Langchain4j application framework. It offers features like registration & login, multi-session support, image generation, prompt words, quota control, knowledge base, model-based search, model switching, and search engine switching. The project integrates models like ChatGPT 3.5, Tongyi Qianwen, Wenxin Yiyuan, Ollama, and DALL-E 2. The backend uses technologies like JDK 17, Spring Boot 3.0.5, Langchain4j, and PostgreSQL with pgvector extension, while the frontend is built with Vue3, TypeScript, and PNPM.

pyLLMSearch is an advanced RAG system that offers a convenient question-answering system with a simple YAML-based configuration. It enables interaction with multiple collections of local documents, with improvements in document parsing, hybrid search, chat history, deep linking, re-ranking, customizable embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) from OpenAI or installed locally. It supports various document formats, incremental embedding updates, dense and sparse embeddings, multiple embedding models, 'Retrieve and Re-rank' strategy, HyDE (Hypothetical Document Embeddings), multi-querying, chat history, and interaction with embedded documents using different models. It also offers simple CLI and web interfaces, deep linking, offline response saving, and an experimental API.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

This repository focuses on Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). It provides code and resources to augment LLMs with real-time data for dynamic, context-aware applications. The content covers topics such as semantic search, fine-tuning embeddings, building RAG chatbots, evaluating LLMs, and using knowledge graphs in RAG. Prerequisites include Python skills, knowledge of machine learning and LLMs, and introductory experience with NLP and AI models.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system. The core features of SGLang include: - **A Flexible Front-End Language**: This allows for easy programming of LLM applications with multiple chained generation calls, advanced prompting techniques, control flow, multiple modalities, parallelism, and external interaction. - **A High-Performance Runtime with RadixAttention**: This feature significantly accelerates the execution of complex LLM programs by automatic KV cache reuse across multiple calls. It also supports other common techniques like continuous batching and tensor parallelism.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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