Best AI tools for< Improve Data Analysis >

Seudo is a data workflow automation platform that uses AI to help businesses automate their data processes. It provides a variety of features to help businesses with data integration, data cleansing, data transformation, and data analysis. Seudo is designed to be easy to use, even for businesses with no prior experience with AI. It offers a drag-and-drop interface that makes it easy to create and manage data workflows. Seudo also provides a variety of pre-built templates that can be used to get started quickly.

Grapha.ai is a data exploration tool that helps users to quickly and easily explore and understand their data. It provides a variety of features to help users to visualize their data, identify trends and patterns, and make informed decisions. Grapha.ai is designed to be easy to use, even for users with no prior experience with data analysis.

PageWise AI is an AI application that provides instant insight into Confluence data by turning tedious searches and repetitive questions into intuitive AI-powered conversations directly in Slack. It offers effortless integration with Slack and Confluence accounts, eliminates the need for sifting through pages, reduces repetitive queries, connects hidden insights within Confluence pages, boosts communication, and scales with business needs. PageWise AI offers flexible pricing plans catering to different team sizes and requirements, with the option for custom integrations, data sources, and self-hosted solutions for enterprise users.

SunDevs is an AI application that specializes in solving business problems to create exceptional customer experiences. The platform offers various AI solutions for chat and messaging, phone and voice interactions, as well as talent solutions like staff augmentation and product teams. SunDevs caters to industries such as ecommerce, cinema, telco, and more, providing integrations with popular platforms like Hubspot, Salesforce, and Zendesk. The application leverages AI technology to process vast amounts of customer data quickly and accurately, identifying patterns and trends that may be overlooked by human analysts. With features like AI chatbots for 24/7 support, personalized interactions, and efficient customer service, SunDevs aims to enhance customer satisfaction and loyalty.

Coefficient is a data connector tool for Google Sheets and Excel that empowers users to unlock the full potential of their spreadsheets by providing live connections to over 60 business systems. It offers features such as two-way syncing, automatic refreshes, and scheduled alerts, all powered by AI. Coefficient streamlines data import, visualization, and system updates, enabling users to eliminate manual work, improve data accuracy, and save time. Trusted by over 50,000 companies, Coefficient is a user-friendly tool that enhances data analysis and reporting processes.

involve.me is an AI-powered platform that allows users to create high-converting forms, surveys, quizzes, and calculators for interactive user flows that boost website conversions. It offers a wide range of pre-built templates and customization options to fit brand identities. The platform provides features like AI form generation, quiz creation, survey building, and analytics insights to help businesses collect quality data and increase user engagement. With involve.me, users can automate sales processes, personalize customer interactions, and grow their email list effortlessly.

SimplyPut is an AI-powered data analytics platform that allows users to ask questions about their data in natural language and get instant answers. It is designed to be intuitive and easy to use, and it can be integrated with a variety of data sources. SimplyPut is used by businesses of all sizes to improve their data literacy and make better decisions.

Kanaries is an augmented analytics platform that uses AI to automate the process of data exploration and visualization. It offers a variety of features to help users quickly and easily find insights in their data, including: * **RATH:** An AI-powered engine that can automatically generate insights and recommendations based on your data. * **Graphic Walker:** A visual analytics tool that allows you to explore your data in a variety of ways, including charts, graphs, and maps. * **Data Painter:** A data cleaning and transformation tool that makes it easy to prepare your data for analysis. * **Causal Analysis:** A tool that helps you identify and understand the causal relationships between variables in your data. Kanaries is designed to be easy to use, even for users with no prior experience with data analysis. It is also highly scalable, so it can be used to analyze large datasets. Kanaries is a valuable tool for anyone who wants to quickly and easily find insights in their data. It can be used by businesses of all sizes, and it is particularly well-suited for organizations that are looking to improve their data-driven decision-making.

Lettria is a no-code AI platform for text that helps users turn unstructured text data into structured knowledge. It combines the best of Large Language Models (LLMs) and symbolic AI to overcome current limitations in knowledge extraction. Lettria offers a suite of APIs for text cleaning, text mining, text classification, and prompt engineering. It also provides a Knowledge Studio for building knowledge graphs and private GPT models. Lettria is trusted by large organizations such as AP-HP and Leroy Merlin to improve their data analysis and decision-making processes.

Lume AI is an AI-powered data mapping application that automates the process of mapping, cleaning, and validating data in various workflows. It offers an all-in-one suite for building pipelines, onboarding customer data, and providing AI-powered insights for data analysis. Users can choose between a no-code platform and API integration to streamline their data mapping processes. Lume AI ensures data security with enterprise-grade encryption and access controls, eliminating the need for manual data mapping. The application is designed to save time and improve efficiency in data management tasks.

SalesMonk is an AI-powered platform designed to supercharge lead generation by utilizing AI agents to maximize meetings. The platform offers AI agents that engage with attendees before, during, and after events to ensure productive conversations. SalesMonk provides tools for identifying and strategizing leads, commanding meetings, discovering leads, personalizing messaging, and managing meetings efficiently. With SalesMonk, users can automate various sales tasks, research leads for personalized approaches, follow up and nurture leads strategically, streamline meetings, and improve performance through data analysis. The platform operates 24/7 on autopilot, helping users widen their reach, land more clients, and create growth around the clock.

HappyLoop is an AI tool that allows SaaS users to embed their own AI Analyst into their products. It provides real-time AI analysis inside the product, enabling users to access data instantly, break down silos, generate reports quickly, and receive actionable recommendations. HappyLoop has been praised for modernizing products, improving user retention, increasing revenue, streamlining workflows, and enhancing user engagement. The tool aims to solve the problem of users struggling to make sense of their data by turning data into decisions instantly. It offers features like real-time insights, advanced reporting, conversational actions, personalized UIs, secure & compliant data handling, and GenAI capabilities.

Connecterra is an intelligent data platform designed specifically for the dairy industry. It provides farmers, advisors, and enterprises with a comprehensive suite of tools to collect, analyze, and visualize their farm data. With advanced AI capabilities, Connecterra helps users identify trends, make informed decisions, and improve their overall operational efficiency.

Connecterra is an intelligent data platform designed specifically for the dairy industry. It provides farmers, advisors, and enterprises with a comprehensive suite of tools to collect, analyze, and visualize their farm data. With advanced AI capabilities, Connecterra helps users identify trends, optimize decision-making, and improve overall farm performance.

Perception AI is an AI tool designed to enhance user experience by providing personalized recommendations and insights based on user behavior and preferences. The tool utilizes advanced algorithms to analyze data and generate tailored suggestions for users. With a focus on improving engagement and satisfaction, Perception AI aims to revolutionize the way users interact with digital platforms. By leveraging artificial intelligence, the tool offers a seamless and intuitive experience for users across various domains.

Dystr is an AI-powered analysis tool that helps businesses make better decisions. It uses machine learning and natural language processing to analyze data and identify trends and patterns. Dystr can be used to analyze a variety of data sources, including text, images, and videos. It can also be used to analyze data from social media, customer surveys, and other sources.

Viable is an AI-driven platform that provides actionable insights from qualitative data. It effortlessly transforms raw data into valuable information using AI technology. The platform offers integrations with various tech stacks and transparent pricing options to meet user requirements. Viable is designed to help businesses improve customer experience, boost employee engagement, enhance marketing strategies, prioritize product management actions, and conduct efficient research with the help of AI.

Jeda.ai is a generative AI workspace that allows users to create, visualize, and analyze data in a collaborative environment. It offers a variety of features, including AI template analysis, AI mind map diagrams, AI flowchart diagrams, AI wireframe, AI text writer, AI sticky notes, AI art, AI vision and transform, AI data analysis, AI document analysis, and AI business cases. Jeda.ai is designed to help users improve their productivity and make better decisions.

Supascout is an AI-powered tool designed to analyze App Store reviews and provide actionable insights to help developers understand user feedback, prioritize features, and address pain-points. By leveraging AI technology, Supascout helps users gain a competitive edge by making informed decisions based on comprehensive data analysis. The tool scours through the most recent reviews in specific regions to offer valuable insights and recommendations for app improvement.

Quizalize is an AI-powered educational platform designed to help teachers differentiate and track student mastery. It offers whole class quiz games, smart quizzes with personalization, and instant mastery data to address learning loss. With features like creating quizzes in seconds, question bank creation, and personalized feedback, Quizalize aims to enhance student engagement and learning outcomes.

FiftyOne is an open-source tool designed for building high-quality datasets and computer vision models. It supercharges machine learning workflows by enabling users to visualize datasets, interpret models faster, and improve efficiency. With FiftyOne, users can explore scenarios, identify failure modes, visualize complex labels, evaluate models, find annotation mistakes, and much more. The tool aims to streamline the process of improving machine learning models by providing a comprehensive set of features for data analysis and model interpretation.

Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates tasks such as data cleaning, preprocessing, and complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. It aims to expedite the data analysis process, making it accessible to all, regardless of their expertise in data analysis. The tool is built to empower users to process data and achieve high-quality visualizations with unparalleled efficiency, and to execute high-performance modeling with the best strategies. Future enhancements include Natural Language Processing (NLP), neural networks, and object detection utilizing YOLO, broadening its capabilities to meet diverse data analysis needs.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

LLM4DB is a repository focused on the intersection of Large Language Models (LLMs) and Database technologies. It covers various aspects such as data processing, data analysis, database optimization, and data management for LLMs. The repository includes research papers, tools, and techniques related to leveraging LLMs for tasks like data cleaning, entity matching, schema matching, data discovery, NL2SQL, data exploration, data visualization, knob tuning, query optimization, and database diagnosis.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

Chat2DB is an AI-driven data development and analysis platform that enables users to communicate with databases using natural language. It supports a wide range of databases, including MySQL, PostgreSQL, Oracle, SQLServer, SQLite, MariaDB, ClickHouse, DM, Presto, DB2, OceanBase, Hive, KingBase, MongoDB, Redis, and Snowflake. Chat2DB provides a user-friendly interface that allows users to query databases, generate reports, and explore data using natural language commands. It also offers a variety of features to help users improve their productivity, such as auto-completion, syntax highlighting, and error checking.

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.

moai is a PyTorch-based AI Model Development Kit (MDK) designed to improve data-driven model workflows, design, and understanding. It offers modularity via monads for model building blocks, reproducibility via configuration-based design, productivity via a data-driven domain modelling language (DML), extensibility via plugins, and understanding via inter-model performance and design aggregation. The tool provides specific integrated actions like play, train, evaluate, plot, diff, and reprod to support heavy data-driven workflows with analytics, knowledge extraction, and reproduction. moai relies on PyTorch, Lightning, Hydra, TorchServe, ONNX, Visdom, HiPlot, Kornia, Albumentations, and the wider open-source community for its functionalities.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

Open-DocLLM is an open-source project that addresses data extraction and processing challenges using OCR and LLM technologies. It consists of two main layers: OCR for reading document content and LLM for extracting specific content in a structured manner. The project offers a larger context window size compared to JP Morgan's DocLLM and integrates tools like Tesseract OCR and Mistral for efficient data analysis. Users can run the models on-premises using LLM studio or Ollama, and the project includes a FastAPI app for testing purposes.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

ERAG is an advanced system that combines lexical, semantic, text, and knowledge graph searches with conversation context to provide accurate and contextually relevant responses. This tool processes various document types, creates embeddings, builds knowledge graphs, and uses this information to answer user queries intelligently. It includes modules for interacting with web content, GitHub repositories, and performing exploratory data analysis using various language models.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

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.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

A curated list of 50+ resources to help you become a Generative AI Data Scientist. This repository includes resources on building GenAI applications with Large Language Models (LLMs), and deploying LLMs and GenAI with Cloud-based solutions.

I analyze and integrate missing clusters in data for coherent structuring.

it helps me analyze serp results and data from certain websites in order to create an outline for the writer

I optimize dish discoverability using data analysis.

TikTok users data analysis and interpretation. (@apify actions)(Email me if any inquiries)

Data analysis and reporting assistant

Expert data analyst with a focus on diabetes data structure analysis.

Expert in healthcare data analysis, coding, and clinical trials.

I am a digital scientist skilled in Python, here to assist with scientific and data analysis tasks.

SAP Logistics expert with a focus on data analysis, project management, and accurate information.

Retrieves hunting-related data for each state. Providing insightful data analysis on trends in hunting statistics. (beta)

Enhances cannabis delivery customer experience with data analysis and recommendations.

Our college entrance exam prediction app uses advanced algorithms and data analysis to provide accurate predictions for students preparing to take their college entrance exams.

Expert math tutor and problem solver with online research and data analysis skills.

Asistente amable y motivador en análisis de datos públicos en compra pública

School Advisory with Enhanced Dataset Analysis

Collecte et analyse de données sur la résilience aux catastrophes naturelles.

Analyzes customer churn and provides strategies to improve loyalty and retention.

This Open AI tool analyzes your financial data, budgets and cashflow and suggests areas of improvement and quick insights. Drop an XLS file here or copy/paste your financial data and get insights! (Your data remains private and creator of this ChatGPT has no access to it).

To master Unified Communications Data across platforms like Cisco, Avaya, Mitel, and Microsoft Teams, by orchestrating a team of expert agents and providing actionable solutions.

Analyzes CME evaluation data for insights.