Best AI tools for< Process Evaluation Data >

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.

Future AGI is a revolutionary AI data management platform that aims to achieve 99% accuracy in AI applications across software and hardware. It provides a comprehensive evaluation and optimization platform for enterprises to enhance the performance of their AI models. Future AGI offers features such as creating trustworthy, accurate, and responsible AI, 10x faster processing, generating and managing diverse synthetic datasets, testing and analyzing agentic workflow configurations, assessing agent performance, enhancing LLM application performance, monitoring and protecting applications in production, and evaluating AI across different modalities.

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.

Reppls is an AI Interview Agents tool designed for data-driven hiring processes. It helps companies interview all applicants to identify the right talents hidden behind uninformative CVs. The tool offers seamless integration with daily tools, such as Zoom and MS Teams, and provides deep technical assessments in the early stages of hiring, allowing HR specialists to focus on evaluating soft skills. Reppls aims to transform the hiring process by saving time spent on screening, interviewing, and assessing candidates.

Weights & Biases is an AI tool that offers documentation, guides, tutorials, and support for using AI models in applications. The platform provides two main products: W&B Weave for integrating AI models into code and W&B Models for building custom AI models. Users can access features such as tracing, output evaluation, cost estimates, hyperparameter sweeps, model registry, and more. Weights & Biases aims to simplify the process of working with AI models and improving model reproducibility.

Deal Protectors is an AI-driven website designed to help users evaluate their car deals to ensure they are getting the best possible price. The platform allows users to upload their deal or input essential details for analysis by an advanced AI machine. Deal Protectors aims to provide transparency, empowerment, and community support in the car purchasing process by comparing deals with national and regional averages. Additionally, the website features a protection forum for sharing dealership experiences, a game called 'Beat The Dealer,' and testimonials from satisfied customers.

Resume Screening AI is an AI application designed to help recruiters, hiring managers, and HR managers screen resumes in bulk efficiently and accurately. By leveraging AI algorithms, the tool automates the screening process, saving time and improving the quality of hire. It offers benefits such as time and cost savings, improved accuracy, enhanced objectivity, and a better candidate experience. The tool uses end-to-end encryption for data security and stores resume file fingerprints and parsed text for easy retrieval. With a focus on optimizing the recruitment process, Resume Screening AI is a transformative solution for businesses looking to attract and identify the most suitable candidates.

YellowGoose is an AI tool that offers intelligent analysis of resumes. It utilizes advanced algorithms to extract and analyze key information from resumes, helping users to streamline the recruitment process. With YellowGoose, users can quickly evaluate candidates and make informed hiring decisions based on data-driven insights.

LlamaIndex is a framework for building context-augmented Large Language Model (LLM) applications. It provides tools to ingest and process data, implement complex query workflows, and build applications like question-answering chatbots, document understanding systems, and autonomous agents. LlamaIndex enables context augmentation by combining LLMs with private or domain-specific data, offering tools for data connectors, data indexes, engines for natural language access, chat engines, agents, and observability/evaluation integrations. It caters to users of all levels, from beginners to advanced developers, and is available in Python and Typescript.

InterviewQueue is an AI-powered online assessment software platform that revolutionizes the recruitment process. It offers customizable coding challenges, insightful AI analytics, and seamless API integration for efficient hiring. With features like custom assessments, AI evaluation, and API integration, InterviewQueue aims to streamline the recruitment process and provide objective evaluations. The platform helps in making data-driven hiring decisions, optimizing the interview process, and enhancing the candidate experience. InterviewQueue focuses on efficiency, customization, objective evaluation, data-driven decisions, and candidate-centric assessments.

AI Elections Accord is a tech accord aimed at combating the deceptive use of AI in the 2024 elections. It sets expectations for managing risks related to deceptive AI election content on large-scale platforms. The accord focuses on prevention, provenance, detection, responsive protection, evaluation, public awareness, and resilience to safeguard the democratic process. It emphasizes collective efforts, education, and the development of defensive tools to protect public debate and build societal resilience against deceptive AI content.

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

HappyML is an AI tool designed to assist users in machine learning tasks. It provides a user-friendly interface for running machine learning algorithms without the need for complex coding. With HappyML, users can easily build, train, and deploy machine learning models for various applications. The tool offers a range of features such as data preprocessing, model evaluation, hyperparameter tuning, and model deployment. HappyML simplifies the machine learning process, making it accessible to users with varying levels of expertise.

Teammately is an AI tool that redefines how Human AI-Engineers build AI. It is an Agentic AI for AI development process, designed to enable Human AI-Engineers to focus on more creative and productive missions in AI development. Teammately follows the best practices of Human LLM DevOps and offers features like Development Prompt Engineering, Knowledge Tuning, Evaluation, and Optimization to assist in the AI development process. The tool aims to revolutionize AI engineering by allowing AI AI-Engineers to handle technical tasks, while Human AI-Engineers focus on planning and aligning AI with human preferences and requirements.

LifeShack is an AI-powered job search tool that revolutionizes the job application process. It automates job searching, evaluation, and application submission, saving users time and increasing their chances of landing top-notch opportunities. With features like automatic job matching, AI-optimized cover letters, and tailored resumes, LifeShack streamlines the job search experience and provides peace of mind to job seekers.

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

Weavel is an AI tool designed to revolutionize prompt engineering for large language models (LLMs). It offers features such as tracing, dataset curation, batch testing, and evaluations to enhance the performance of LLM applications. Weavel enables users to continuously optimize prompts using real-world data, prevent performance regression with CI/CD integration, and engage in human-in-the-loop interactions for scoring and feedback. Ape, the AI prompt engineer, outperforms competitors on benchmark tests and ensures seamless integration and continuous improvement specific to each user's use case. With Weavel, users can effortlessly evaluate LLM applications without the need for pre-existing datasets, streamlining the assessment process and enhancing overall performance.

Rawbot is an AI model comparison tool designed to simplify the selection process by enabling users to identify and understand the strengths and weaknesses of various AI models. It allows users to compare AI models based on performance optimization, strengths and weaknesses identification, customization and tuning, cost and efficiency analysis, and informed decision-making. Rawbot is a user-friendly platform that supports a wide range of popular and emerging AI models, making it a premier destination for researchers, developers, and business leaders to make informed decisions about AI models that best fit their needs.

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.

LEVI AI Recruiting Software is a modern recruitment automation platform powered by artificial intelligence. It revolutionizes the candidate evaluation and selection process by using advanced AI recruitment tools. LEVI assists in making data-driven hiring decisions, matches candidates to job requirements, conducts independent interviews, generates comprehensive reports, integrates with hiring systems, and enables informed and efficient hiring decisions. The application unlocks the full potential of machine learning models, eliminates bias in the hiring process, and automates candidate screening. LEVI's AI-powered recruitment tools change how candidate evaluations are performed through automated resume screening, candidate sourcing, and AI interview assessments.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

Nejumi Leaderboard 3 is a comprehensive evaluation platform for large language models, assessing general language capabilities and alignment aspects. The evaluation framework includes metrics for language processing, translation, summarization, information extraction, reasoning, mathematical reasoning, entity extraction, knowledge/question answering, English, semantic analysis, syntactic analysis, alignment, ethics/moral, toxicity, bias, truthfulness, and robustness. The repository provides an implementation guide for environment setup, dataset preparation, configuration, model configurations, and chat template creation. Users can run evaluation processes using specified configuration files and log results to the Weights & Biases project.

Awesome Text2SQL is a curated repository containing tutorials and resources for Large Language Models, Text2SQL, Text2DSL, Text2API, Text2Vis, and more. It provides guidelines on converting natural language questions into structured SQL queries, with a focus on NL2SQL. The repository includes information on various models, datasets, evaluation metrics, fine-tuning methods, libraries, and practice projects related to Text2SQL. It serves as a comprehensive resource for individuals interested in working with Text2SQL and related technologies.

xFinder is a model specifically designed for key answer extraction from large language models (LLMs). It addresses the challenges of unreliable evaluation methods by optimizing the key answer extraction module. The model achieves high accuracy and robustness compared to existing frameworks, enhancing the reliability of LLM evaluation. It includes a specialized dataset, the Key Answer Finder (KAF) dataset, for effective training and evaluation. xFinder is suitable for researchers and developers working with LLMs to improve answer extraction accuracy.

RAGLAB is a modular, research-oriented open-source framework for Retrieval-Augmented Generation (RAG) algorithms. It offers reproductions of 6 existing RAG algorithms and a comprehensive evaluation system with 10 benchmark datasets, enabling fair comparisons between RAG algorithms and easy expansion for efficient development of new algorithms, datasets, and evaluation metrics. The framework supports the entire RAG pipeline, provides advanced algorithm implementations, fair comparison platform, efficient retriever client, versatile generator support, and flexible instruction lab. It also includes features like Interact Mode for quick understanding of algorithms and Evaluation Mode for reproducing paper results and scientific research.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

AutoRAG is an AutoML tool designed to automatically find the optimal RAG pipeline for your data. It simplifies the process of evaluating various RAG modules to identify the best pipeline for your specific use-case. The tool supports easy evaluation of different module combinations, making it efficient to find the most suitable RAG pipeline for your needs. AutoRAG also offers a cloud beta version to assist users in running and optimizing the tool, along with building RAG evaluation datasets for a starting price of $9.99 per optimization.

The UltraRAG framework is a researcher and developer-friendly RAG system solution that simplifies the process from data construction to model fine-tuning in domain adaptation. It introduces an automated knowledge adaptation technology system, supporting no-code programming, one-click synthesis and fine-tuning, multidimensional evaluation, and research-friendly exploration work integration. The architecture consists of Frontend, Service, and Backend components, offering flexibility in customization and optimization. Performance evaluation in the legal field shows improved results compared to VanillaRAG, with specific metrics provided. The repository is licensed under Apache-2.0 and encourages citation for support.

AI Audio Datasets List (AI-ADL) is a comprehensive collection of datasets consisting of speech, music, and sound effects, used for Generative AI, AIGC, AI model training, and audio applications. It includes datasets for speech recognition, speech synthesis, music information retrieval, music generation, audio processing, sound synthesis, and more. The repository provides a curated list of diverse datasets suitable for various AI audio tasks.

The DALM (Domain Adapted Language Modeling) toolkit is designed to unify general LLMs with vector stores to ground AI systems in efficient, factual domains. It provides developers with tools to build on top of Arcee's open source Domain Pretrained LLMs, enabling organizations to deeply tailor AI according to their unique intellectual property and worldview. The toolkit contains code for fine-tuning a fully differential Retrieval Augmented Generation (RAG-end2end) architecture, incorporating in-batch negative concept alongside RAG's marginalization for efficiency. It includes training scripts for both retriever and generator models, evaluation scripts, data processing codes, and synthetic data generation code.

This repository contains the code for the paper 'LESS: Selecting Influential Data for Targeted Instruction Tuning'. The work proposes a data selection method to choose influential data for inducing a target capability. It includes steps for warmup training, building the gradient datastore, selecting data for a task, and training with the selected data. The repository provides tools for data preparation, data selection pipeline, and evaluation of the model trained on the selected data.

LightRAG is a repository hosting the code for LightRAG, a system that supports seamless integration of custom knowledge graphs, Oracle Database 23ai, Neo4J for storage, and multiple file types. It includes features like entity deletion, batch insert, incremental insert, and graph visualization. LightRAG provides an API server implementation for RESTful API access to RAG operations, allowing users to interact with it through HTTP requests. The repository also includes evaluation scripts, code for reproducing results, and a comprehensive code structure.

OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Its main features include: * Comprehensive support for models and datasets: Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 70+ datasets with about 400,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. * Efficient distributed evaluation: One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. * Diversified evaluation paradigms: Support for zero-shot, few-shot, and chain-of-thought evaluations, combined with standard or dialogue-type prompt templates, to easily stimulate the maximum performance of various models. * Modular design with high extensibility: Want to add new models or datasets, customize an advanced task division strategy, or even support a new cluster management system? Everything about OpenCompass can be easily expanded! * Experiment management and reporting mechanism: Use config files to fully record each experiment, and support real-time reporting of results.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

VC startup analyst for in-depth startup evaluations

输入需求内容，多个需求逐行显示，前面用1. 2. 3.开始，自动生成软件功能点评估表

Ensures product quality through systematic process monitoring and evaluation.

FactCheck Navigator GPT is designed for in-depth fact checking and analysis of written content and evaluation of its source. The approach is to iterate through predefined and well-prompted steps. If desired, the user can refine the process by providing input between these steps.

AI OKR Coach is a tool designed to assist users in the process of creating and assessing OKR (Objectives and Key Results). It provides a structured and flexible approach to OKR setting and evaluation.

Teste: que nota receberia o processo de recrutamento e seleção de vendedores na sua empresa?

Upload your process map and I will analyse and suggest improvements

Friendly process engineer for Coda.io

Guides clear BPMN process design with ASCII art

Optimizes production processes for improved efficiency and quality.

Optimizes business operations through process enhancements.

Optimizes production processes for efficient large-scale operations.

Improves operational efficiency by optimizing processes and reducing waste.

Discover Process Talks: Your Next Investment!

Optimizes manufacturing processes for efficiency and quality.

Emulating Whitehead's insights on 'Process and Reality'

Process Documentation for serving GPTs.

Trademark Process Assistant, Not an Attorney & Definitely Not Legal Advice (independently verify info received). Gain insights on U.S. trademark process & concepts, USPTO resources, application steps & more - all while being reminded of the importance of consulting legal pros 4 specific guidance.

Structured process for prioritizing marketing tasks based on strategic alignment. Outputs in Eisenhower, RACI and other methodologies.