Best AI tools for< Extract Structured Insights >

Jsonify is an AI tool that automates the process of exploring and understanding websites to find, filter, and extract structured data at scale. It uses AI-powered agents to navigate web content, replacing traditional data scrapers and providing data insights with speed and precision. Jsonify integrates with leading data analysis and business intelligence suites, allowing users to visualize and gain insights into their data easily. The tool offers a no-code dashboard for creating workflows and easily iterating on data tasks. Jsonify is trusted by companies worldwide for its ability to adapt to page changes, learn as it runs, and provide technical and non-technical integrations.

Podwise is an AI-powered podcast tool that helps users extract structured knowledge from podcasts. It offers features such as AI-powered summarization, mind mapping, outlining, transcription, and integration with popular knowledge management tools. Podwise aims to enhance the podcast listening experience by providing users with a more efficient and effective way to learn and retain information from podcasts.

ThreadScribe.ai is a chatbot for Slack that uses cutting-edge AI to transform your Slack conversations into a structured, easily searchable knowledge base. Effortlessly organize your Slack discussions into searchable knowledge with one click. It utilizes cutting-edge AI to summarize lengthy discussions into concise, actionable insights, saving you time and enhancing decision-making. ThreadScribe.ai integrates effortlessly with Slack, ensuring a smooth transition from casual conversations to structured knowledge without disrupting your workflow. It transforms the maze of Slack channels into a navigable treasure trove of knowledge, helping you find the signal through the noise.

The FileAI website offers an AI-powered file reading assistant that specializes in data extraction from structured documents like financial statements, legal documents, and research papers. It automates tasks related to legal and compliance review, finance and accounting report preparation, and research and academia support. The tool aims to streamline document processing, enhance learning processes, and improve research efficiency. With features like summarizing complex texts, extracting key information, and detecting plagiarism, FileAI caters to users in various industries and educational fields. The platform prioritizes data security and user privacy, ensuring that data is used solely for its intended purpose and deleted after 7 days of non-use.

Dataku.ai is an advanced data extraction and analysis tool powered by AI technology. It offers seamless extraction of valuable insights from documents and texts, transforming unstructured data into structured, actionable information. The tool provides tailored data extraction solutions for various needs, such as resume extraction for streamlined recruitment processes, review insights for decoding customer sentiments, and leveraging customer data to personalize experiences. With features like market trend analysis and financial document analysis, Dataku.ai empowers users to make strategic decisions based on accurate data. The tool ensures precision, efficiency, and scalability in data processing, offering different pricing plans to cater to different user needs.

The website is an AI tool designed to generate a knowledge graph based on input text. It uses advanced algorithms and machine learning capabilities to streamline operations, deliver personalized experiences, and unlock new possibilities. Users can input text related to various topics, and the tool processes the information to create a structured knowledge graph.

Isomeric is an AI tool that utilizes artificial intelligence to semantically understand unstructured text and extract specific data. It transforms messy, unstructured text into machine-readable JSON, enabling users to easily gather insights, process data, and deliver results. From web scraping to browser extensions to general information extraction, Isomeric helps users scale their data gathering pipeline in seconds.

Docugami is an AI-powered document engineering platform that enables business users to extract, analyze, and automate data from various types of documents. It empowers users with immediate impact without the need for extensive machine learning investments or IT development. Docugami's proprietary Business Document Foundation Model and Generative AI technology transform unstructured text and tables into structured information, allowing users to unlock insights, increase productivity, and ensure compliance.

Docugami is an AI-powered document engineering platform that enables business users to extract, analyze, and automate data from various types of documents. It empowers users with immediate impact without the need for extensive machine learning investments or IT development. Docugami's proprietary Business Document Foundation Model leverages Generative AI to transform unstructured text into structured information, allowing users to unlock insights and drive business processes efficiently.

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.

Otio is an AI research and writing partner powered by o3-mini, Claude 3.7, and Gemini 2.0. It offers a fast and efficient way to do research by summarizing and chatting with documents, writing and editing in an AI text editor, and automating workflows. Otio is trusted by over 200,000 researchers and students, providing detailed, structured AI summaries, automatic summaries for various types of content, chat capabilities, and workflow automation. Users can extract insights from research quickly, automate repetitive tasks, and edit their writing with AI assistance.

Mgmate is an AI-powered application designed for managers to provide feedback and support to their teams. The platform offers features such as suggested agenda topics, speech-to-text updates, and AI filters to extract key ideas from past syncs. Mgmate aims to streamline communication, enhance productivity, and improve team dynamics through structured feedback mechanisms and AI-driven insights.

PandasAI is an open-source AI tool designed for conversational data analysis. It allows users to ask questions in natural language to their enterprise data and receive real-time data insights. The tool is integrated with various data sources and offers enhanced analytics, actionable insights, detailed reports, and visual data representation. PandasAI aims to democratize data analysis for better decision-making, offering enterprise solutions for stable and scalable internal data analysis. Users can also fine-tune models, ingest universal data, structure data automatically, augment datasets, extract data from websites, and forecast trends using AI.

Podwise is an AI-powered podcast tool designed for podcast lovers to extract structured knowledge from episodes at 10x speed. It offers features such as AI-powered summarization, mind mapping, content outlining, transcription, and seamless integration with knowledge management workflows. Users can subscribe to favorite content, get lightning-speed access to structured knowledge, and discover episodes of interest. Podwise aims to address the challenge of enjoying podcasts, recalling less, and forgetting quickly, by providing a meticulous, accurate, and impactful tool for efficient podcast referencing and note consolidation.

TurboDoc is an AI-powered tool designed to extract information from invoices and transform unstructured data into easy-to-read structured data. It offers a user-friendly interface for efficient work with accounts payable, budget planning, and control. The tool ensures high accuracy through advanced AI models and provides secure data storage with AES256 encryption. Users can automate invoice processing, link Gmail for seamless integration, and optimize workflow with various applications.

NuMind is an AI tool designed to solve information extraction tasks efficiently. It offers high-quality lightweight models tailored to users' needs, automating classification, entity recognition, and structured extraction. The tool is powered by task-specific and domain-agnostic foundation models, outperforming GPT-4 and similar models. NuMind provides solutions for various industries such as insurance and healthcare, ensuring privacy, cost-effectiveness, and faster NLP projects.

Airparser is an AI-powered email and document parser tool that revolutionizes data extraction by utilizing the GPT parser engine. It allows users to automate the extraction of structured data from various sources such as emails, PDFs, documents, and handwritten texts. With features like automatic extraction, export to multiple platforms, and support for multiple languages, Airparser simplifies data extraction processes for individuals and businesses. The tool ensures data security and offers seamless integration with other applications through APIs and webhooks.

Parsio is an AI-powered document parser that can extract structured data from PDFs, emails, and other documents. It uses natural language processing to understand the context of the document and identify the relevant data points. Parsio can be used to automate a variety of tasks, such as extracting data from invoices, receipts, and emails.

Monkt is a powerful document processing platform that transforms various document formats into AI-ready Markdown or structured JSON. It offers features like instant conversion of PDF, Word, PowerPoint, Excel, CSV, web pages, and raw HTML into clean markdown format optimized for AI/LLM systems. Monkt enables users to create intelligent applications, custom AI chatbots, knowledge bases, and training datasets. It supports batch processing, image understanding, LLM optimization, and API integration for seamless document processing. The platform is designed to handle document transformation at scale, with support for multiple file formats and custom JSON schemas.

Imaginary Programming is an AI tool that allows frontend developers to leverage OpenAI's GPT engine to add human-like intelligence to their code effortlessly. By defining function prototypes in TypeScript, developers can access GPT's capabilities without the need for AI model training. The tool enables users to extract structured data, generate text, classify data based on intent or emotion, and parse unstructured language. Imaginary Programming is designed to help developers tackle new challenges and enhance their projects with AI intelligence.

OneKE is a flexible dockerized system for schema-guided knowledge extraction, capable of extracting information from the web and raw PDF books across multiple domains like science and news. It employs a collaborative multi-agent approach and includes a user-customizable knowledge base to enable tailored extraction. OneKE offers various IE tasks support, data sources support, LLMs support, extraction method support, and knowledge base configuration. Users can start with examples using YAML, Python, or Web UI, and perform tasks like Named Entity Recognition, Relation Extraction, Event Extraction, Triple Extraction, and Open Domain IE. The tool supports different source formats like Plain Text, HTML, PDF, Word, TXT, and JSON files. Users can choose from various extraction models like OpenAI, DeepSeek, LLaMA, Qwen, ChatGLM, MiniCPM, and OneKE for information extraction tasks. Extraction methods include Schema Agent, Extraction Agent, and Reflection Agent. The tool also provides support for schema repository and case repository management, along with solutions for network issues. Contributors to the project include Ningyu Zhang, Haofen Wang, Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, and Huajun Chen.

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

Crawl4AI is a powerful and free web crawling service that extracts valuable data from websites and provides LLM-friendly output formats. It supports crawling multiple URLs simultaneously, replaces media tags with ALT, and is completely free to use and open-source. Users can integrate Crawl4AI into Python projects as a library or run it as a standalone local server. The tool allows users to crawl and extract data from specified URLs using different providers and models, with options to include raw HTML content, force fresh crawls, and extract meaningful text blocks. Configuration settings can be adjusted in the `crawler/config.py` file to customize providers, API keys, chunk processing, and word thresholds. Contributions to Crawl4AI are welcome from the open-source community to enhance its value for AI enthusiasts and developers.

This repository is an awesome list dedicated to resources for using Large Language Models (LLMs) to generate JSON or other structured outputs. It includes terminology explanations, hosted and local models, Python libraries, blog articles, videos, Jupyter notebooks, and leaderboards related to LLMs and JSON generation. The repository covers various aspects such as function calling, JSON mode, guided generation, and tool usage with different providers and models.

Toolio is an OpenAI-like HTTP server API implementation that supports structured LLM response generation, making it conform to a JSON schema. It is useful for reliable tool calling and agentic workflows based on schema-driven output. Toolio is based on the MLX framework for Apple Silicon, specifically M1/M2/M3/M4 Macs. It allows users to host MLX-format LLMs for structured output queries and provides a command line client for easier usage of tools. The tool also supports multiple tool calls and the creation of custom tools for specific tasks.

KG-LLM-Papers is a repository that collects papers integrating knowledge graphs (KGs) and large language models (LLMs). It serves as a comprehensive resource for research on the role of KGs in the era of LLMs, covering surveys, methods, and resources related to this integration.

This solution accelerator is built on Azure Cognitive Search Service and Azure OpenAI Service to synthesize post-contact center transcripts for intelligent contact center scenarios. It converts raw transcripts into customer call summaries to extract insights around product and service performance. Key features include conversation summarization, key phrase extraction, speech-to-text transcription, sensitive information extraction, sentiment analysis, and opinion mining. The tool enables data professionals to quickly analyze call logs for improvement in contact center operations.

Dwata is a desktop application that allows users to chat with any AI model and gain insights from their data. Chats are organized into threads, similar to Discord, with each thread connecting to a different AI model. Dwata can connect to databases, APIs (such as Stripe), or CSV files and send structured data as prompts when needed. The AI's response will often include SQL or Python code, which can be used to extract the desired insights. Dwata can validate AI-generated SQL to ensure that the tables and columns referenced are correct and can execute queries against the database from within the application. Python code (typically using Pandas) can also be executed from within Dwata, although this feature is still in development. Dwata supports a range of AI models, including OpenAI's GPT-4, GPT-4 Turbo, and GPT-3.5 Turbo; Groq's LLaMA2-70b and Mixtral-8x7b; Phind's Phind-34B and Phind-70B; Anthropic's Claude; and Ollama's Llama 2, Mistral, and Phi-2 Gemma. Dwata can compare chats from different models, allowing users to see the responses of multiple models to the same prompts. Dwata can connect to various data sources, including databases (PostgreSQL, MySQL, MongoDB), SaaS products (Stripe, Shopify), CSV files/folders, and email (IMAP). The desktop application does not collect any private or business data without the user's explicit consent.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

Codellm-devkit (CLDK) is a Python library that serves as a multilingual program analysis framework bridging traditional static analysis tools and Large Language Models (LLMs) specialized for code (CodeLLMs). It simplifies the process of analyzing codebases across multiple programming languages, enabling the extraction of meaningful insights and facilitating LLM-based code analysis. The library provides a unified interface for integrating outputs from various analysis tools and preparing them for effective use by CodeLLMs. Codellm-devkit aims to enable the development and experimentation of robust analysis pipelines that combine traditional program analysis tools and CodeLLMs, reducing friction in multi-language code analysis and ensuring compatibility across different tools and LLM platforms. It is designed to seamlessly integrate with popular analysis tools like WALA, Tree-sitter, LLVM, and CodeQL, acting as a crucial intermediary layer for efficient communication between these tools and CodeLLMs. The project is continuously evolving to include new tools and frameworks, maintaining its versatility for code analysis and LLM integration.

TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.

This repository is a collection of papers on Tabular Large Language Models (LLMs) specialized for processing tabular data. It includes surveys, models, and applications related to table understanding tasks such as Table Question Answering, Table-to-Text, Text-to-SQL, and more. The repository categorizes the papers based on key ideas and provides insights into the advancements in using LLMs for processing diverse tables and fulfilling various tabular tasks based on natural language instructions.

The Intelligence Toolkit is a suite of interactive workflows designed to help domain experts make sense of real-world data by identifying patterns, themes, relationships, and risks within complex datasets. It utilizes generative AI (GPT models) to create reports on findings of interest. The toolkit supports analysis of case, entity, and text data, providing various interactive workflows for different intelligence tasks. Users are expected to evaluate the quality of data insights and AI interpretations before taking action. The system is designed for moderate-sized datasets and responsible use of personal case data. It uses the GPT-4 model from OpenAI or Azure OpenAI APIs for generating reports and insights.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

ComfyUI-Ollama-Describer is an extension for ComfyUI that enables the use of LLM models provided by Ollama, such as Gemma, Llava (multimodal), Llama2, Llama3, or Mistral. It requires the Ollama library for interacting with large-scale language models, supporting GPUs using CUDA and AMD GPUs on Windows, Linux, and Mac. The extension allows users to run Ollama through Docker and utilize NVIDIA GPUs for faster processing. It provides nodes for image description, text description, image captioning, and text transformation, with various customizable parameters for model selection, API communication, response generation, and model memory management.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. It combines the speed of ripgrep with the code-aware parsing of tree-sitter to deliver precise results with complete code blocks, making it perfect for large codebases and AI-driven development workflows. Probe is fully local, keeping code on the user's machine without relying on external APIs. It supports multiple languages, offers various search options, and can be used in CLI mode, MCP server mode, AI chat mode, and web interface. The tool is designed to be flexible, fast, and accurate, providing developers and AI models with full context and relevant code blocks for efficient code exploration and understanding.

Call Center AI is an AI-powered call center solution leveraging Azure and OpenAI GPT. It allows for AI agent-initiated phone calls or direct calls to the bot from a configured phone number. The bot is customizable for various industries like insurance, IT support, and customer service, with features such as accessing claim information, conversation history, language change, SMS sending, and more. The project is a proof of concept showcasing the integration of Azure Communication Services, Azure Cognitive Services, and Azure OpenAI for an automated call center solution.

Structured analyzer of business case PDFs with tailored insights.

Analyzes email headers for security insights, presenting data in a structured table view.

I create structured filenames for PDFs from OCR text.

Generate a structured abstract for academic papers, primarily in the field of biology, adhering to a specified word count range. Simply upload your manuscript file (without the abstract) and specify the word count (for example, '200-250') to GPT.

This prompt is structured to provide an effective methodology in generating progressively more detailed and specific summaries, focused on key entities.

Generate a Structured description in typescript format from the image and generate an image from that description.　and OCR

I am a dog. Bite me

用于执法日志

お好みの美女を教えてください

熟练掌握各类python代码的编制

Extracts and structures web content in formal, technical language.

自动完成清晰易解的PPT内容提取和结构化

Specializes in analyzing ChatGPT conversations for code snippets to build apps

I extract data and tables from PDFs to CSV, focusing on data privacy and precision.

Extract the essence of the novel story according to the quantity requirements and generate corresponding images. The images can be used directly to create novel videos.小说推文图片自动批量生成,可自动生成风格一致性图片

Extract insights seamlessly from diverse documents.

Extract from receipts to CSV: Date of Purchase, Item Purchased, Quantity Purchased, Units

PDFChat : Analyse 1000's of PDF's in seconds, extract and chat with PDFs in any language.

Analyze watch images, extract text, and craft sales descriptions. Add 1 or more images for a single watch to get started.

Put your prompt engineering skills to the ultimate test! Embark on a journey to outwit a mythical guardian of ancient secrets. Try to extract the secret passphrase hidden in the system prompt and enter it in chat when you think you have it and claim your glory. Good luck!