Best AI tools for< Compare Data Accuracy >

Basejump AI is an AI-powered data access tool that allows users to interact with their database using natural language queries. It empowers teams to access data quickly and easily, providing instant insights and eliminating the need to navigate through complex dashboards. With Basejump AI, users can explore data, save relevant information, create custom collections, and refine datapoints to meet their specific requirements. The tool ensures data accuracy by allowing users to compare datapoints side by side. Basejump AI caters to various industries such as healthcare, HR, and software, offering real-time insights and analytics to streamline decision-making processes and optimize workflow efficiency.

The Shark Risk Forecast App by SafeWaters.ai is an innovative application that provides 7-day shark risk forecasts for beaches worldwide with 83% accuracy. Utilizing predictive AI technology trained on extensive shark attack and marine weather data, the app aims to enhance beach safety by alerting users to potential risks. In addition to current and future risk forecasts, the app offers features like Shark Spotting Drones Live Feed, Chatbot interaction, and Tagged Shark Tracking for a comprehensive beach safety experience.

Talking Tree is an AI-powered document management tool designed for legal professionals to digitize and manage legal documents efficiently. It offers advanced OCR technology and a custom RAG architecture to convert printed and handwritten text into searchable structured data. The platform enables users to find information, draft agreements, and analyze legacy documents with unprecedented speed and accuracy. Talking Tree provides a secure and user-friendly interface with multilingual support, making it a valuable resource for legal research and document management.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

NEX is a controllable AI image generation tool designed for product creative image suite. It offers a variety of multimodal controls, IP-consistent models, and team workspaces to bring ideas to life. With fine-grained controls like pose, color, and character consistency, NEX supports any creative task. It provides tailored generative media models for various applications, private and custom-built AI models, and collaborative workspaces for secure data sharing. NEX is ideal for creative enterprises in media & entertainment, gaming, fashion, and more, offering up to 10x cost reduction in model development compared to competitors.

Extracto.bot is an AI web scraping tool that automates the process of extracting data from websites. It is a no-configuration, intelligent web scraper that allows users to collect data from any site using Google Sheets and AI technology. The tool is designed to be simple, instant, and intelligent, enabling users to save time and effort in collecting and organizing data for various purposes.

ClearSeas.AI is an AI-powered market research visualization dashboard that leverages advanced AI technologies to interpret complex datasets, delivering intuitive and visually compelling insights. The platform offers services such as expert survey design, efficient survey deployment, B2B panel access, and full-service project management. ClearSeas.AI prioritizes data security by employing stringent protocols, advanced encryption technologies, and document protection measures. The platform does not use customer data for AI training, ensuring data privacy and confidentiality. ClearSeas.AI aims to illuminate insights with the power of AI, empowering users to stay ahead of market trends and make informed strategic decisions.

Aethera is a collaborative knowledge discovery platform that leverages advanced AI models to help teams and individuals understand documents, YouTube videos, and websites without the need to read them. It offers powerful features for organizing, personalizing, and discovering information, along with document management tools, multilingual support, and the ability to summarize and compare multiple documents. Aethera also allows users to create personalized AI assistants, chat with sets of documents using personas, and work collaboratively within organizations. The platform is designed to streamline knowledge discovery processes and boost productivity by providing tailored insights and summaries from various sources.

Beloga is a knowledge operating system (OS) for teams that instantly unifies tools and information, boosting productivity through seamless collaboration and real-time search. It uses AI to deliver precise, actionable insights from team data, enabling quick, informed decision-making. Beloga streamlines team workflows into a single platform, eliminating app-switching and enhancing collaboration and efficiency. It also offers multi-source integration, allowing users to easily compare and integrate data from multiple sources, revealing hidden insights. Beloga's features include hyper-contextualized key insights, seamless integration, cross-referencing made easy, and instant access to the information you need.

OSS Insight is an AI tool that provides deep insight into developers and repositories on GitHub, offering information about stars, pull requests, issues, pushes, comments, and reviews. It utilizes artificial intelligence to analyze data and provide valuable insights to users. The tool ranks repositories, tracks trending repositories, and offers real-time information about GitHub events. Additionally, it offers features like data exploration, collections, live blog, API integration, and widgets.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

ChatPlayground AI is a versatile platform that allows users to compare multiple AI chatbots to obtain the best responses. With 14+ AI apps and features available, users can achieve better AI answers 73% of the time. The platform offers a comprehensive prompt library, real-time web search capabilities, image generation, history recall, document upload and analysis, and multilingual support. It caters to developers, data scientists, students, researchers, content creators, writers, and AI enthusiasts. Testimonials from users highlight the efficiency and creativity-enhancing benefits of using ChatPlayground AI.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Ai Tool Hunt is a comprehensive directory of free AI tools, software, and websites. It provides users with a curated list of the best AI resources available online, empowering them to enhance their digital experiences and leverage the latest advancements in artificial intelligence. With Ai Tool Hunt, users can discover powerful AI tools for various tasks, including content creation, data analysis, image editing, language learning, and more. The platform offers detailed descriptions, user ratings, and easy access to these tools, making it a valuable resource for individuals and businesses seeking to integrate AI into their workflows.

Humbi AI is an AI-powered platform offering actuarial services for healthcare organizations, health plans, provider organizations, and drug manufacturers. The platform combines data science and actuarial expertise to provide competitive intelligence analytics, helping clients identify opportunities and manage risks effectively. Humbi AI offers a range of services from data management to actuarial design, incorporating knowledge from various fields such as medical science and technology. The platform includes tools for Medicare Advantage strategy building, provider performance comparison, pharmacy management, and access to medical and pharmacy data for millions of members.

Lilac is an AI tool designed to enhance data quality and exploration for AI applications. It offers features such as data search, quantification, editing, clustering, semantic search, field comparison, and fuzzy-concept search. Lilac enables users to accelerate dataset computations and transformations, making it a valuable asset for data scientists and AI practitioners. The tool is trusted by Alignment Lab and is recommended for working with LLM datasets.

Zelma is an AI-powered research assistant that enables users to find, graph, and understand U.S. school testing data using plain English queries. It allows users to search student test data by school district, demographics, grade, and more, and presents the results with graphs, tables, and descriptions. Zelma aims to make education data accessible and understandable for everyone.

Staypia is an AI-powered hotel reservation platform that offers incredible discounts on hotel bookings worldwide. With over 3.16M hotels in its database, Staypia provides users with the best prices collected by its AI server. Members can enjoy additional discounts and exclusive deals, making Staypia a top choice for affordable and convenient hotel reservations.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

EDA GPT is an open-source data analysis companion that offers a comprehensive solution for structured and unstructured data analysis. It streamlines the data analysis process, empowering users to explore, visualize, and gain insights from their data. EDA GPT supports analyzing structured data in various formats like CSV, XLSX, and SQLite, generating graphs, and conducting in-depth analysis of unstructured data such as PDFs and images. It provides a user-friendly interface, powerful features, and capabilities like comparing performance with other tools, analyzing large language models, multimodal search, data cleaning, and editing. The tool is optimized for maximal parallel processing, searching internet and documents, and creating analysis reports from structured and unstructured data.

PyCM is a Python library for multi-class confusion matrices, providing support for input data vectors and direct matrices. It is a comprehensive tool for post-classification model evaluation, offering a wide range of metrics for predictive models and accurate evaluation of various classifiers. PyCM is designed for data scientists who require diverse metrics for their models.

Cosdata is a cutting-edge AI data platform designed to power the next generation search pipelines. It features immutability, version control, and excels in semantic search, structured knowledge graphs, hybrid search capabilities, real-time search at scale, and ML pipeline integration. The platform is customizable, scalable, efficient, enterprise-grade, easy to use, and can manage multi-modal data. It offers high performance, indexing, low latency, and high requests per second. Cosdata is designed to meet the demands of modern search applications, empowering businesses to harness the full potential of their data.

SuperKnowa is a fast framework to build Enterprise RAG (Retriever Augmented Generation) Pipelines at Scale, powered by watsonx. It accelerates Enterprise Generative AI applications to get prod-ready solutions quickly on private data. The framework provides pluggable components for tackling various Generative AI use cases using Large Language Models (LLMs), allowing users to assemble building blocks to address challenges in AI-driven text generation. SuperKnowa is battle-tested from 1M to 200M private knowledge base & scaled to billions of retriever tokens.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

This repository contains the code and data for the paper 'Vision Language Models Are Blind'. It explores the limitations of large language models with vision capabilities (VLMs) in performing basic visual tasks that are easy for humans. The repository presents benchmark results showcasing the poor performance of state-of-the-art VLMs on tasks like counting line intersections, identifying circles, letters, and shapes, and following color-coded paths. The research highlights the challenges faced by VLMs in understanding visual information accurately, drawing parallels to myopia and blindness in human vision.

LongLoRA is a tool for efficient fine-tuning of long-context large language models. It includes LongAlpaca data with long QA data collected and short QA sampled, models from 7B to 70B with context length from 8k to 100k, and support for GPTNeoX models. The tool supports supervised fine-tuning, context extension, and improved LoRA fine-tuning. It provides pre-trained weights, fine-tuning instructions, evaluation methods, local and online demos, streaming inference, and data generation via Pdf2text. LongLoRA is licensed under Apache License 2.0, while data and weights are under CC-BY-NC 4.0 License for research use only.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

Ask: "What graphs can you create from MY pee times data file (.xlsx)?" Show to your Urologist.

Apples vs. Oranges? Optimize your low-calorie diet. Compare food items. Get tailored advice on satiating, nutritious, cost-effective food choices based on 240 items.

Provide TOPIC & different views to compare: Gateway to Informed Comparisons. Harness AI-powered insights to analyze and score different viewpoints on any topic, delivering balanced, data-driven perspectives for smarter decision-making.

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Your Gateway to AI Discovery – Ask, Compare, Learn. Explore AI tools and software with ease. Create AI Tech Stacks for your business and much more – Just ask, and AI Hub will do the rest!

Your go-to film data guru

Compare similar GPTs outputs quality on the same question, identify the most valuable one.

Find the perfect online course in tech, business, marketing, programming, and more. Compare options from top platforms like Udemy, Coursera, and EDX.

I provide monthly salary data in euros, using a structured format for global job roles.

Compares and reports differences in text files

Fix mismatched entries in your data

AquaAirAI is a specialized assistant that compares air and water quality across cities and regions, providing insightful reports and recommendations based on comprehensive environmental data analysis from Excel files.

Export comparison data from GSC to get a scatter plot of keyword rankings before and after an update.

Explore social and environmental data for 169 countries to measure social progress and go beyond GDP. Using data from the Social Progress Imperative and powered by Open AI.

Backed by the best MLB data engines! Get current and historical statistics for players, teams and games.

Material facts application that details the content, impact category data, and environmental rating of materials.

Azeroth Data Sage: A detailed Warcraft Log analysis with direct API access. Give the Sage link to a log, ask a question, and the Data Sage will provide!

Using public SBA data to help you find the best fit lender for your small business

Blending user insight with online data for tailored marijuana reviews.

Query BCorp company data. All data is publicly available. United Kingdom only (for now).