Best AI tools for< analysis >

The International Institute of Business Analysis (IIBA) website is a global standard platform that offers resources, certifications, and best practices for business analysts. It provides a curated body of knowledge to advance careers in business analysis and enable successful organizational change. The website features tools, templates, and expert insights to enhance business analysis practices and drive value through artificial intelligence integration.

AI SWOT Analysis Generator is an AI-powered tool that helps you create a SWOT Analysis for your company based on its description. It identifies the Strengths, Weaknesses, Opportunities, and Threats of your company and provides a comprehensive understanding of its current state and potential for future growth or challenges.

This website provides a tool that uses artificial intelligence (AI) to analyze a user's facial features and determine their face shape. The tool is free to use and requires users to upload a photo of themselves. The AI algorithm then analyzes the photo and identifies the user's face shape. The website also provides information about the five most common face shapes and how to determine your own face shape. Additionally, the website includes a section with frequently asked questions (FAQs) about the face shape analysis tool.

The AI PESTEL Analysis Generator is a tool that helps businesses understand and evaluate the external macro-environmental factors that can affect their operations. The acronym PESTEL stands for Political, Economic, Social, Technological, Environmental, and Legal factors. By conducting a PESTEL analysis, businesses can gain a better understanding of the external factors that can affect their operations, and use this information to develop strategies and plans to adapt and succeed in the marketplace.

Binary Vulnerability Analysis is an AI-powered binary vulnerability scanner. It decompiles a binary file and checks it against more than 20k historical vulnerabilities. The analysis could take up to 10 minutes and proceeds as following: Decompile the Executable using Ghidra Remove filler, clean, format, guess symbols and inline function calls Generate function-wise embeddings using a finetuned CodeT5+ Embedding model. Finetuned on Big Clone Bench CodeSearchNet Check similarities against DiverseVul Dataset Check for vulnerabilities with SemGrep

This website is an AI tool that helps senior developers analyze AI tech debt. AI tech debt is the technical debt that accumulates when AI systems are developed and deployed. It can be difficult to identify and quantify AI tech debt, but it can have a significant impact on the performance and reliability of AI systems. This tool uses a variety of techniques to analyze AI tech debt, including static analysis, dynamic analysis, and machine learning. It can help senior developers to identify and quantify AI tech debt, and to develop strategies to reduce it.

TrendSpider is a comprehensive trading software suite designed for traders by traders. It offers a wide range of tools and features to help users make informed trading decisions, including charting and analysis tools, trade idea generation tools, strategy development tools, and trade timing and execution tools. TrendSpider also provides access to a variety of third-party indicators, scanners, and strategies. The platform is available as a web-based application and as a mobile app for iOS and Android devices.

Roic AI is an AI tool designed to provide users with essential financial data for analyzing companies. It offers comprehensive company summaries, 30+ years of financial statements, and earnings call transcripts in a single location. Users can access crucial information about popular companies like Apple Inc. and Microsoft Corporation through this platform.

Valossa is an AI video analysis tool that offers a range of products for automating video workflows, including transcription, content logging, and sentiment analysis. It leverages multimodal AI for recognizing video, image, and audio content, providing features like automated captions, brand-safe advertising, clip previews, sensitive content identification, and mood analysis. Valossa aims to accelerate video productivity by offering customized AI solutions tailored to specific use cases. The tool is designed to streamline video analysis processes, improve content management, and enhance user experiences through intelligent automation.

The website offers a service where users can have their resumes analyzed by an AI tool. Users can submit their resumes to receive feedback and suggestions for improvement. The AI tool evaluates various aspects of the resume, such as formatting, content, and keywords, to provide personalized recommendations. It aims to help individuals enhance their resumes and increase their chances of landing job interviews.

Luminal is a powerful AI copilot that enables users to clean, transform, and analyze spreadsheets 10x faster. It offers fast and efficient data analysis capabilities, allowing users to perform complex operations, answer sophisticated questions, and run AI-enabled tasks using natural language. Luminal is designed to simplify data processing tasks, providing users with the superpowers of a professional data analyst without the complexity. The application supports multiple languages, secure data hosting, and data encryption to ensure data privacy and security.

Census GPT is an AI tool that provides data analysis services based on census information in the USA. The tool offers insights on crime rates, demographics, income levels, education levels, and population statistics. Users can ask specific questions related to these categories and receive detailed answers generated by the AI model.

Cvify is an AI-powered resume analysis and optimization tool that helps job seekers create tailored, high-impact resumes and cover letters. It provides personalized enhancements, pinpoints key strengths and skills, and identifies areas for improvement. With Cvify, users can quickly and easily optimize their resumes to match specific job descriptions, increasing their chances of landing their dream job.

Castello.ai is a financial analysis tool that uses artificial intelligence to help businesses make better decisions. It provides users with real-time insights into their financial data, helping them to identify trends, risks, and opportunities. Castello.ai is designed to be easy to use, even for those with no financial background.

Keyword Catcher is a powerful SERP analysis and keyword research tool that makes it easy to gain actionable insights from Google results. With Keyword Catcher, you can quickly and easily generate hundreds of long-tail keywords, filter your results to only include the keywords that you know you can rank for, and get a comprehensive view of the SERPs to understand the dynamics of keyword rankings. Keyword Catcher also includes a number of AI-powered features, such as AI-generated post outlines, that can help you to create high-quality content that ranks well in search results.

Nutri Vista is an AI-powered nutrition analysis tool that simplifies the process of understanding your meals' nutritional content. By simply describing your meals, Nutri Vista provides detailed insights, making it effortless to make informed and healthy choices. Its user-friendly dashboard allows you to track your progress and stay motivated on your wellness journey. Additionally, you can share your profile publicly to inspire and connect with others who share your passion for healthy living.

MASCAA is a comprehensive human confidence analysis platform that focuses on evaluating the confidence of users through video and audio during various tasks. It integrates advanced facial expression and voice analysis technologies to provide valuable feedback for students, instructors, individuals, businesses, and teams. MASCAA offers quick and easy test creation, evaluation, and confidence assessment for educational settings, personal use, startups, small organizations, universities, and large organizations. The platform aims to unlock long-term value and enhance customer experience by helping users assess and improve their confidence levels.

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.

Gardian is an AI tool designed to streamline content analysis processes by leveraging advanced AI technology. It allows users to create custom AI Agents with specific labels to detect and manage content that violates company policies. Gardian offers pre-configured models, custom analysis labels, a simple API for integration, multilanguage support, transparent pricing, and privacy protection. It serves various use cases such as content moderation, live chat moderation, and customer sentiment analysis, providing valuable insights and enhancing user experience.

InsightFace is an open-source deep face analysis library that provides a rich variety of state-of-the-art algorithms for face recognition, detection, and alignment. It is designed to be efficient for both training and deployment, making it suitable for research institutions and industrial organizations. InsightFace has achieved top rankings in various challenges and competitions, including the ECCV 2022 WCPA Challenge, NIST-FRVT 1:1 VISA, and WIDER Face Detection Challenge 2019.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

DataFrame is a C++ analytical library designed for data analysis similar to libraries in Python and R. It allows you to slice, join, merge, group-by, and perform various statistical, summarization, financial, and ML algorithms on your data. DataFrame also includes a large collection of analytical algorithms in form of visitors, ranging from basic stats to more involved analysis. You can easily add your own algorithms as well. DataFrame employs extensive multithreading in almost all its APIs, making it suitable for analyzing large datasets. Key principles followed in the library include supporting any type without needing new code, avoiding pointer chasing, having all column data in contiguous memory space, minimizing space usage, avoiding data copying, using multi-threading judiciously, and not protecting the user against garbage in, garbage out.

OAD is a powerful open-source tool for analyzing and visualizing data. It provides a user-friendly interface for exploring datasets, generating insights, and creating interactive visualizations. With OAD, users can easily import data from various sources, clean and preprocess data, perform statistical analysis, and create customizable visualizations to communicate findings effectively. Whether you are a data scientist, analyst, or researcher, OAD can help you streamline your data analysis workflow and uncover valuable insights from your data.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in Video Analysis. It assesses the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. The dataset comprises 900 videos with 256 hours and 2,700 human-annotated question-answer pairs. It distinguishes itself through features like duration variety, diversity in video types, breadth in data modalities, and quality in annotations.

Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, and more. It provides Interactive Broker connectivity via ib_async and includes major Python packages for statistical and time series analysis. The image is optimized for size, includes jedi language server, jupyterlab-lsp, and common command line utilities. Users can install new packages with sudo, leverage apt cache, and bring their own dot files and SSH keys. The tool is designed for ephemeral containers, ensuring data persistence and flexibility for quantitative analysis tasks.

The NZT Poker AI Bot is an advanced tool designed to revolutionize poker gameplay by providing comprehensive features to dominate 17 rooms with the Cash Fish Monitor. It offers detailed analysis of opponents' VPIP and PFR, extensive hand history database, opponent exploitation techniques, data-driven intelligence, player profiles, advanced neural network technology, expert training, continuous refinement, and cutting-edge algorithms for maximizing poker profits. Created by a team of poker experts, this AI tool continuously adapts to the latest poker strategies and utilizes state-of-the-art technology to enhance decision-making prowess.

DeepBI is an AI-native data analysis platform that leverages the power of large language models to explore, query, visualize, and share data from any data source. Users can use DeepBI to gain data insight and make data-driven decisions.

This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

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.

DataAgent is a multi-agent system for data analysis, capable of understanding data development and data analysis requirements, understanding data, and generating SQL and Python code for tasks such as data query, data visualization, and machine learning.

Nightshade Antidote is an image forensics tool used to analyze digital images for signs of manipulation or forgery. It implements several common techniques used in image forensics including metadata analysis, copy-move forgery detection, frequency domain analysis, and JPEG compression artifacts analysis. The tool takes an input image, performs analysis using the above techniques, and outputs a report summarizing the findings.

airda(Air Data Agent) is a multi-agent system for data analysis, which can understand data development and data analysis requirements, understand data, and generate SQL and Python code for data query, data visualization, machine learning and other tasks.

Nerority's Advanced GPT Suite is a collection of 33 GPTs that can be controlled with natural language prompts. The suite includes tools for various tasks such as strategic consulting, business analysis, career profile building, content creation, educational purposes, image-based tasks, knowledge engineering, marketing, persona creation, programming, prompt engineering, role-playing, simulations, and task management. Users can access links, usage instructions, and guides for each GPT on their respective pages. The suite is designed for public demonstration and usage, offering features like meta-sequence optimization, AI priming, prompt classification, and optimization. It also provides tools for generating articles, analyzing contracts, visualizing data, distilling knowledge, creating educational content, exploring topics, generating marketing copy, simulating scenarios, managing tasks, and more.

Scikit-LLM is a tool that seamlessly integrates powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks. It allows users to leverage large language models for various text analysis applications within the familiar scikit-learn framework. The tool simplifies the process of incorporating advanced language processing capabilities into machine learning pipelines, enabling users to benefit from the latest advancements in natural language processing.

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.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

cassIO is a framework-agnostic Python library that seamlessly integrates Apache Cassandra with ML/LLM/genAI workloads. It provides an easy-to-use interface for developers to connect their Cassandra databases to machine learning models, allowing them to perform complex data analysis and AI-powered tasks directly on their Cassandra data. cassIO is designed to be flexible and extensible, making it suitable for a wide range of use cases, from data exploration and visualization to predictive modeling and natural language processing.

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.

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TRIZ-based Function Analysis

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A mentor in analysis, linking maths to real-world applications, with follow-up questions for deeper understanding.

Creates full DOCX data analysis reports with integrated visualizations

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Analyzes data to influence strategic decision-making processes.

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Technical analyst backed with 1W-1D-4H refreshed financial market data. For more timeframes and granularity please check our website.

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Upload disclosure documents, and I will summarize what's going on, identify red flag areas to look closer at, and answer all Q&A!

This prompt is designed to act as an expert psychologist in personality analysis, using three different psychological models: the Big Five Factor model, the Myers-Briggs Sixteen Types model, and the Enneagram model.

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Save time by analyzing what SEO issues you should fix.

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