Best AI tools for< Reduce Recruitment Costs >

Skillsminer is a Workforce Intelligence Platform that helps businesses bridge the growth gap by unlocking dynamic and scalable workforce potential. The platform utilizes AI technology to provide valuable insights and data to identify, develop, and utilize skills and talent effectively. Skillsminer supports various industries such as utilities, financial sector, education, housing associations, health sector, and government in recruiting hidden talent, maximizing internal mobility, and enhancing career opportunities. The platform aims to help businesses hire smarter, improve workforce agility, and make informed learning and development decisions to drive sustainable growth and business success while optimizing costs.

Hiremaster.ai is an AI-powered platform designed to help transportation & logistics companies recruit quality candidates efficiently and cost-effectively. The platform harnesses the power of data and AI to optimize hiring processes, reduce costs, and revolutionize recruitment in the T&L industry. Hiremaster.ai offers programmatic AI solutions that automate recruitment ads, optimize hiring goals, and attract qualified drivers through various digital channels. The platform provides both managed service and self-service options, ensuring flexibility and control over recruitment advertising costs. With Hiremaster.ai, users can benefit from AI-driven candidate nurture, end-to-end ROI reporting, and strategic cost-saving solutions tailored for the T&L sector.

Glozo is an AI-powered sourcing platform designed to streamline recruitment processes by matching job requirements to suitable candidates efficiently. The platform utilizes AI to enhance sourcing accuracy, reduce time-to-hire, and minimize costs for recruiters. Glozo offers features such as AI-powered candidate search, automated screening, and access to a vast talent database from various sources. The platform caters to recruiters in the tech industry, finance sector, and other fields, helping them build stronger talent pipelines and make informed hiring decisions.

ResearchGoat is a generative AI-powered research platform that enables clients to conduct in-depth qualitative research and interviews with unprecedented speed and genuine insights. The platform offers shockingly lower costs compared to traditional consumer research, AI moderators that can converse in any language, and the ability to recruit participants and conduct interviews efficiently. ResearchGoat empowers users to focus on understanding their customers by providing comprehensive insights and analysis from real people.

Skima is a recruitment AI and talent intelligence platform that aims to revolutionize the recruitment process by leveraging AI technology to help recruiters find the perfect candidates quickly and efficiently. The platform offers features such as AI-powered candidate assessment, seamless ATS integration, and smart job description generation to streamline the hiring process and improve recruitment ROI. Skima enables recruiters to unlock valuable insights from candidate databases, diversify client reach, and identify talent based on client demands. With Skima, recruiters can save time, increase productivity, and make data-driven hiring decisions.

Popp is an AI-driven recruitment solution that revolutionizes talent acquisition by making hiring faster, fairer, and more human. The platform offers seamless integrations with leading ATS platforms, pre-trained AI assistants, and data-driven insights to streamline the recruitment process. Popp empowers recruiters to manage higher volumes of candidates while improving the candidate experience, all at a fraction of the cost. By automating pre-screening conversations and providing personalized AI assistance, Popp helps reduce time-to-hire, increase hiring efficiency, and enhance candidate satisfaction.

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.

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.

AutoScreen is an AI-powered recruitment tool that revolutionizes the hiring process. It utilizes advanced algorithms and machine learning to streamline the recruitment process, saving time and resources for businesses. With AutoScreen, employers can efficiently screen and shortlist candidates based on predefined criteria, leading to faster and more accurate hiring decisions. The tool offers a user-friendly interface, customizable features, and seamless integration with existing HR systems, making it a valuable asset for modern recruitment practices.

JobXRecruiter is an AI-powered CV review tool designed for recruiters to streamline the candidate evaluation process. It automates the review of resumes, provides detailed candidate analysis, and helps recruiters save time by focusing on hiring rather than manual screening. The tool offers a 1-minute setup, reduces candidate evaluation time, and eliminates tedious screening tasks. With JobXRecruiter, recruiters can create projects for each vacancy, receive match scores for candidates, and easily shortlist the best candidates without opening individual CVs. The application is secure, efficient, and a game-changer for recruiters looking to optimize their hiring process.

The website offers an AI talent intelligence suite to streamline and automate recruitment processes. It provides advanced AI-powered solutions for talent matching, equitable hiring, talent analytics, conversational AI, and API integration. The platform aims to enhance recruitment efficiency, reduce bias, and improve candidate experience through automation and predictive analytics.

Mosaictrack is an AI-powered platform that revolutionizes the recruitment process by leveraging artificial intelligence to match candidates with suitable job opportunities. The platform offers a range of products and features, including diversity enhancement, chatbot engagement, and machine learning-based recruiting. Mosaictrack aims to streamline the hiring process, reduce bias, and improve candidate experience through innovative AI technologies.

MTestHub is an all-in-one recruiting and assessment automation solution that streamlines the hiring process for organizations. It harnesses AI technology to identify top talent, provides an authentic online examination platform, and offers features such as auto screening, shortlisting, efficient interviews, and post-shortlisting actions. The platform aims to enhance recruitment efficiency, improve candidate experience, and reduce administrative burdens through automation and data-driven insights.

Metaview is an AI-powered tool that automates note-taking for interviews, intake meetings, and candidate debriefs. It captures and structures interview details, saving time for recruiters to focus on high-quality interactions with candidates and hiring teams. Metaview integrates with various tools and platforms, ensuring a seamless workflow. It prioritizes security and compliance, complying with regulations like GDPR and CCPA. The tool is designed specifically for recruiting, enhancing the recruitment process by customizing notes, automating scorecards, and facilitating debrief sessions.

ProRank is an AI-powered platform that empowers organizations in the healthcare industry to find and hire top talent with precision and efficiency. The platform combines advanced technology with user-friendly features to streamline the recruitment process, ensuring efficient and effective candidate connections. ProRank offers a curated talent database, AI talent CRM, talent analytics, and key integrations with various ATS vendors, enabling organizations to make data-driven decisions and reduce unconscious bias in recruitment.

BarRaiser is an Interview Intelligence Platform designed to help companies make better hiring decisions. It offers AI-powered tools for structured interviews, interviewer training, and interview platform services. The platform aims to streamline the recruitment process by providing live interview guidance, interview quality reports, hiring recommendations, and real-time feedback. BarRaiser's AI Interview Notes feature leverages artificial intelligence to enhance recruitment processes and talent assessment. The platform is trusted by over 500 customers worldwide and has proven metrics for savings, speed, and precision in the hiring process.

HiPeople is an AI-powered platform designed to streamline the hiring process by providing resume screening, assessments, and reference checks for fast, unbiased, and efficient hiring decisions. It offers a comprehensive toolkit for modern teams to automate mundane tasks, reduce time-to-fill, and make data-driven hiring choices. With a focus on skills-based hiring and volume hiring, HiPeople aims to help organizations identify and hire top talent effectively.

Harver is an AI-powered platform that specializes in predictive assessments and automated solutions for talent acquisition. The platform helps companies make better hiring decisions by prioritizing potential over past experiences, leading to impactful results. Harver offers a range of assessments, including personality questionnaires, cognitive ability testing, situational judgment tests, and more. By leveraging data-driven insights and intelligent automation, Harver streamlines the hiring process, reduces bias, and improves the quality of hires. The platform is designed to enhance the candidate experience, optimize sourcing investments, and provide actionable business insights for informed decision-making.

RecruitGenius.ai is an AI-powered automated recruiting tool designed to streamline and optimize the recruitment process for HR professionals and hiring managers. The platform offers features such as AI auto screening, one-way video interviews, smart direct interview scheduler, talent pool management system, candidate relationship management, analytic reports, and ATS integration. RecruitGenius.ai aims to help organizations secure top talents, reduce time to hire, and simplify the hiring process by providing all necessary tools in one place.

pymetrics is an AI-powered soft skills platform that revolutionizes the hiring and talent management process by leveraging data-driven behavioral insights and audited AI technology. The platform aims to create a more efficient, effective, and fair recruitment process across the talent lifecycle. pymetrics offers solutions for talent acquisition, workforce transformation, mobility, reskilling, learning and development, and soft skills assessment. It provides custom AI algorithms tailored to each company's unique needs, ensuring unbiased candidate evaluation and suggesting optimal job matches.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

ceLLama is a streamlined automation pipeline for cell type annotations using large-language models (LLMs). It operates locally to ensure privacy, provides comprehensive analysis by considering negative genes, offers efficient processing speed, and generates customized reports. Ideal for quick and preliminary cell type checks.

Dockershrink is an AI-powered Commandline Tool designed to help reduce the size of Docker images. It combines traditional Rule-based analysis with Generative AI techniques to optimize Image configurations. The tool supports NodeJS applications and aims to save costs on storage, data transfer, and build times while increasing developer productivity. By automatically applying advanced optimization techniques, Dockershrink simplifies the process for engineers and organizations, resulting in significant savings and efficiency improvements.

Lobe Icons is a collection of popular AI / LLM Model Brand SVG logos and icons. It features lightweight and scalable icons designed with highly optimized scalable vector graphics (SVG) for optimal performance. The collection is tree-shakable, allowing users to import only the icons they need to reduce the overall bundle size of their projects. Lobe Icons has an active community of designers and developers who can contribute and seek support on platforms like GitHub and Discord. The repository supports a wide range of brands across different models, providers, and applications, with more brands continuously being added through contributions. Users can easily install Lobe UI with the provided commands and integrate it with NextJS for server-side rendering. Local development can be done using Github Codespaces or by cloning the repository. Contributions are welcome, and users can contribute code by checking out the GitHub Issues. The project is MIT licensed and maintained by LobeHub.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

Voicechat2 is a fast, fully local AI voice chat tool that uses WebSockets for communication. It includes a WebSocket server for remote access, default web UI with VAD and Opus support, and modular/swappable SRT, LLM, TTS servers. Users can customize components like SRT, LLM, and TTS servers, and run different models for voice-to-voice communication. The tool aims to reduce latency in voice communication and provides flexibility in server configurations.

LMCache is a serving engine extension designed to reduce time to first token (TTFT) and increase throughput, particularly in long-context scenarios. It stores key-value caches of reusable texts across different locations like GPU, CPU DRAM, and Local Disk, allowing the reuse of any text in any serving engine instance. By combining LMCache with vLLM, significant delay savings and GPU cycle reduction are achieved in various large language model (LLM) use cases, such as multi-round question answering and retrieval-augmented generation (RAG). LMCache provides integration with the latest vLLM version, offering both online serving and offline inference capabilities. It supports sharing key-value caches across multiple vLLM instances and aims to provide stable support for non-prefix key-value caches along with user and developer documentation.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

This repository contains codes related to Generative AI as per YouTube video. It includes various notebooks and files for different days covering topics like map reduce, text to SQL, LLM parameters, tagging, and Kaggle competition. The repository also includes resources like PDF files and databases for different projects related to Generative AI.

MaskLLM is a learnable pruning method that establishes Semi-structured Sparsity in Large Language Models (LLMs) to reduce computational overhead during inference. It is scalable and benefits from larger training datasets. The tool provides examples for running MaskLLM with Megatron-LM, preparing LLaMA checkpoints, pre-tokenizing C4 data for Megatron, generating prior masks, training MaskLLM, and evaluating the model. It also includes instructions for exporting sparse models to Huggingface.

The Medical_Image_Analysis repository focuses on X-ray image-based medical report generation using large language models. It provides pre-trained models and benchmarks for CheXpert Plus dataset, context sample retrieval for X-ray report generation, and pre-training on high-definition X-ray images. The goal is to enhance diagnostic accuracy and reduce patient wait times by improving X-ray report generation through advanced AI techniques.

Control large language models and AI tools through voice commands using the Talon Voice dictation engine. This tool is designed to help users quickly edit text, code by voice, reduce keyboard use for those with health issues, and speed up workflow by using AI commands across the desktop. It prompts and extends tools like Github Copilot and OpenAI API for text and image generation. Users can set up the tool by downloading the repo, obtaining an OpenAI API key, and customizing the endpoint URL for preferred models. The tool can be used without an OpenAI key and can be exclusively used with Copilot for those not needing LLM integration.

This repository is for testing routing Claude Code requests to different models. It implements Normal Mode and Router Mode, using various models like qwen2.5-coder-3b-instruct, qwen-max-0125, deepseek-v3, and deepseek-r1. The project aims to reduce the cost of using Claude Code by leveraging free models and KV-Cache. Users can set appropriate ignorePatterns for the project. The Router Mode allows for the separation of tool invocation from coding tasks by using multiple models for different purposes.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

AutoGPTQ is an easy-to-use LLM quantization package with user-friendly APIs, based on GPTQ algorithm (weight-only quantization). It provides a simple and efficient way to quantize large language models (LLMs) to reduce their size and computational cost while maintaining their performance. AutoGPTQ supports a wide range of LLM models, including GPT-2, GPT-J, OPT, and BLOOM. It also supports various evaluation tasks, such as language modeling, sequence classification, and text summarization. With AutoGPTQ, users can easily quantize their LLM models and deploy them on resource-constrained devices, such as mobile phones and embedded systems.

This repository contains an implementation of Ring Attention, a technique for processing large sequences in transformers. Ring Attention splits the data across the sequence dimension and applies ring reduce to the processing of the tiles of the attention matrix, similar to flash attention. It also includes support for Striped Attention, a follow-up paper that permutes the sequence for better workload balancing for autoregressive transformers, and grouped query attention, which saves on communication costs during the ring reduce. The repository includes a CUDA version of the flash attention kernel, which is used for the forward and backward passes of the ring attention. It also includes logic for splitting the sequence evenly among ranks, either within the attention function or in the external ring transformer wrapper, and basic test cases with two processes to check for equivalent output and gradients.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project caches pre-generated model results to reduce response time for similar requests and enhance user experience. It integrates various embedding frameworks and local storage options, offering functionalities like cache-writing, cache-querying, and cache-clearing through RESTful API. The tool supports multi-tenancy, system commands, and multi-turn dialogue, with features for data isolation, database management, and model loading schemes. Future developments include data isolation based on hyperparameters, enhanced system prompt partitioning storage, and more versatile embedding models and similarity evaluation algorithms.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

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