Best AI tools for< Data Processor >

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.

FormX.ai is an AI-powered data extraction and conversion tool that automates the process of extracting data from physical documents and converting it into digital formats. It supports a wide range of document types, including invoices, receipts, purchase orders, bank statements, contracts, HR forms, shipping orders, loyalty member applications, annual reports, business certificates, personnel licenses, and more. FormX.ai's pre-configured data extraction models and effortless API integration make it easy for businesses to integrate data extraction into their existing systems and workflows. With FormX.ai, businesses can save time and money on manual data entry and improve the accuracy and efficiency of their data processing.

Deep Cognition offers PaperEntry AI, an Intelligent Document Processing solution powered by generative AI. It automates data entry tasks with high accuracy, scalability, and configurability, handling complex documents of any type or format. The application is trusted by leading global organizations for customs clearance automation and government document processing, delivering significant time and cost savings. With industry-specific features and a proven track record, Deep Cognition provides a state-of-the-art solution for businesses seeking efficient data extraction and automation.

Kupiks is an automated email parsing tool designed to simplify data entry processes by extracting key information from emails such as customer inquiries, leads, invoices, and more. It eliminates manual entry, saves time, and minimizes errors. The tool offers a simple and seamless solution to automate data entry and streamline workflows, making it ideal for customer support, order management, and expense management.

Base64.ai is an automated document processing API that offers a leading no-code AI solution for understanding documents, photos, and videos. It provides a comprehensive set of features for document processing across various industries, with a strong focus on accuracy, security, and extensibility. Base64.ai is designed to streamline document automation processes and improve data extraction efficiency.

Graphcore is a cloud-based platform that accelerates machine learning processes by harnessing the power of IPU-powered generative AI. It offers cloud services, pre-trained models, optimized inference engines, and APIs to streamline operations and bring intelligence to enterprise applications. With Graphcore, users can build and deploy AI-native products and platforms using the latest AI technologies such as LLMs, NLP, and Computer Vision.

Docsumo is an advanced Document AI platform designed for scalability and efficiency. It offers a wide range of capabilities such as pre-processing documents, extracting data, reviewing and analyzing documents. The platform provides features like document classification, touchless processing, ready-to-use AI models, auto-split functionality, and smart table extraction. Docsumo is a leader in intelligent document processing and is trusted by various industries for its accurate data extraction capabilities. The platform enables enterprises to digitize their document processing workflows, reduce manual efforts, and maximize data accuracy through its AI-powered solutions.

AlgoDocs is a powerful AI Platform developed based on the latest technologies to streamline your processes and free your team from annoying and error-prone manual data entry by offering fast, secure, and accurate document data extraction.

Infrrd is an intelligent document automation platform that offers advanced document extraction solutions. It leverages AI technology to enhance, classify, extract, and review documents with high accuracy, eliminating the need for human review. Infrrd provides effective process transformation solutions across various industries, such as mortgage, invoice, insurance, and audit QC. The platform is known for its world-class document extraction engine, supported by over 10 patents and award-winning algorithms. Infrrd's AI-powered automation streamlines document processing, improves data accuracy, and enhances operational efficiency for businesses.

Cradl AI is a no-code AI-powered document workflow automation tool that helps organizations automate document-related tasks, such as data extraction, processing, and validation. It uses AI to automatically extract data from complex document layouts, regardless of layout or language. Cradl AI also integrates with other no-code tools, making it easy to build and deploy custom AI models.

Super.AI provides Intelligent Document Processing (IDP) solutions powered by Large Language Models (LLMs) and human-in-the-loop (HITL) capabilities. It automates document processing tasks such as data extraction, classification, and redaction, enabling businesses to streamline their workflows and improve accuracy. Super.AI's platform leverages cutting-edge AI models from providers like Amazon, Google, and OpenAI to handle complex documents, ensuring high-quality outputs. With its focus on accuracy, flexibility, and scalability, Super.AI caters to various industries, including financial services, insurance, logistics, and healthcare.

Hailo is a leading provider of top-performing edge AI processors for various edge devices, offering generative AI accelerators, AI vision processors, and AI accelerators. The company's technology enables high-performance deep learning applications on edge devices, catering to industries such as automotive, security, industrial automation, retail, and personal computing.

Veryfi is an OCR API tool for invoice and receipt data extraction. It offers fast, accurate, and secure document capture and data extraction on any type of document. Veryfi empowers users to process documents efficiently, automate manual data entry, and implement AI into various business processes. The tool is designed to streamline workflows, enhance accuracy, and unlock new levels of efficiency across industries such as finance, insurance, and more.

Slicker is a modular payments platform that aims to improve payments success rate, lower transaction costs, and maximize revenue for businesses. It provides a payments infrastructure that integrates with existing setups, offering features like smart routing, global coverage, in-depth analytics, and reconciliation. Slicker helps businesses accept payments seamlessly, make smarter decisions, and enhance the overall payment experience for customers worldwide.

The Value Chain Generator® is an AI & Big Data platform for circular bioeconomy that helps companies, waste processors, and regions maximize the value and minimize the carbon footprint of by-products and waste. It uses global techno-economic and climate intelligence to identify circular opportunities, match with suitable partners and technologies, and create profitable and impactful solutions. The platform accelerates the circular transition by integrating local industries through technology, reducing waste, and increasing profits.

PYQ is an AI-powered platform that helps businesses automate document-related tasks, such as data extraction, form filling, and system integration. It uses natural language processing (NLP) and machine learning (ML) to understand the content of documents and perform tasks accordingly. PYQ's platform is designed to be easy to use, with pre-built automations for common use cases. It also offers custom automation development services for more complex needs.

Autofillr is an AI-powered Chrome extension that automates form filling, making it accurate, secure, and effortless. It utilizes advanced AI technology to understand and learn the structure of web forms, enabling it to detect and fill out a wide variety of forms with precision. Autofillr goes beyond basic fields like names and addresses, aiming to make any form a one-click experience. It continuously enhances its technology to provide a streamlined and efficient online experience for users.

Affinda is a document AI platform that can read, understand, and extract data from any document type. It combines 10+ years of IP in document reconstruction with the latest advancements in computer vision, natural language processing, and deep learning. Affinda's platform can be used to automate a variety of document processing workflows, including invoice processing, receipt processing, credit note processing, purchase order processing, account statement processing, resume parsing, job description parsing, resume redaction, passport processing, birth certificate processing, and driver's license processing. Affinda's platform is used by some of the world's leading organizations, including Google, Microsoft, Amazon, and IBM.

NPI Lookup is an AI-powered platform that offers advanced search and validation services for National Provider Identifier (NPI) numbers of healthcare providers in the United States. The tool uses cutting-edge artificial intelligence technology, including Natural Language Processing (NLP) algorithms and GPT models, to provide comprehensive insights and answers related to NPI profiles. It allows users to search and validate NPI records of doctors, hospitals, and other healthcare providers using everyday language queries, ensuring accurate and up-to-date information from the NPPES NPI database.

ResuMetrics is an AI-powered platform designed to streamline the resume processing workflow. It offers solutions to extract structured data from resumes and automate the anonymization process. The platform provides an easy-to-use API for automating resume analysis, including candidate onboarding and PII redaction. With features like resume scoring and vacancy matching on the roadmap, ResuMetrics aims to enhance the efficiency of resume processing tasks. Users can choose from different subscription plans based on their processing needs, with credits consumed per document page. Overall, ResuMetrics is a comprehensive tool for organizations looking to optimize their resume processing operations.

LEADS is a lightweight embedded assisted driving system designed to simplify the development of instrumentation, control, and analysis systems for racing cars. It is written in Python and C/C++ with impressive performance. The system is customizable and provides abstract layers for component rearrangement. It supports hardware components like Raspberry Pi and Arduino, and can adapt to various hardware types. LEADS offers a modular structure with a focus on flexibility and lightweight design. It includes robust safety features, modern GUI design with dark mode support, high performance on different platforms, and powerful ESC systems for traction control and braking. The system also supports real-time data sharing, live video streaming, and AI-enhanced data analysis for driver training. LEADS VeC Remote Analyst enables transparency between the driver and pit crew, allowing real-time data sharing and analysis. The system is designed to be user-friendly, adaptable, and efficient for racing car development.

KaibanJS is a JavaScript-native framework for building multi-agent AI systems. It enables users to create specialized AI agents with distinct roles and goals, manage tasks, and coordinate teams efficiently. The framework supports role-based agent design, tool integration, multiple LLMs support, robust state management, observability and monitoring features, and a real-time agentic Kanban board for visualizing AI workflows. KaibanJS aims to empower JavaScript developers with a user-friendly AI framework tailored for the JavaScript ecosystem, bridging the gap in the AI race for non-Python developers.

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YAO is an open-source application engine written in Golang, suitable for developing business systems, website/APP API, admin panel, and self-built low-code platforms. It adopts a flow-based programming model to implement functions by writing YAO DSL or using JavaScript. Yao allows developers to create web services by processes, creating a database model, writing API services, and describing dashboard interfaces just by JSON for web & hardware, and 10x productivity. It is based on the flow-based programming idea, developed in Go language, and supports multiple ways to expand the data stream processor. Yao has a built-in data management system, making it suitable for quickly making various management backgrounds, CRM, ERP, and other internal enterprise systems. It is highly versatile, efficient, and performs better than PHP, JAVA, and other languages.

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

LightRAG is a PyTorch library designed for building and optimizing Retriever-Agent-Generator (RAG) pipelines. It follows principles of simplicity, quality, and optimization, offering developers maximum customizability with minimal abstraction. The library includes components for model interaction, output parsing, and structured data generation. LightRAG facilitates tasks like providing explanations and examples for concepts through a question-answering pipeline.

ztachip is a RISCV accelerator designed for vision and AI edge applications, offering up to 20-50x acceleration compared to non-accelerated RISCV implementations. It features an innovative tensor processor hardware to accelerate various vision tasks and TensorFlow AI models. ztachip introduces a new tensor programming paradigm for massive processing/data parallelism. The repository includes technical documentation, code structure, build procedures, and reference design examples for running vision/AI applications on FPGA devices. Users can build ztachip as a standalone executable or a micropython port, and run various AI/vision applications like image classification, object detection, edge detection, motion detection, and multi-tasking on supported hardware.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. It aims to replicate the best-known performance of target model/dataset combinations in optimally-configured hardware environments. The repository will be deprecated upon the publication of v3.2.0 and will no longer be maintained or published.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. The purpose is to quickly replicate complete software environments showcasing the AI capabilities of Intel platforms. It includes optimizations for popular deep learning frameworks like TensorFlow and PyTorch, with additional plugins/extensions for improved performance. The repository is licensed under Apache License Version 2.0.

Swirl is an open-source software that allows users to simultaneously search multiple content sources and receive AI-ranked results. It connects to various data sources, including databases, public data services, and enterprise sources, and utilizes AI and LLMs to generate insights and answers based on the user's data. Swirl is easy to use, requiring only the download of a YML file, starting in Docker, and searching with Swirl. Users can add credentials to preloaded SearchProviders to access more sources. Swirl also offers integration with ChatGPT as a configured AI model. It adapts and distributes user queries to anything with a search API, re-ranking the unified results using Large Language Models without extracting or indexing anything. Swirl includes five Google Programmable Search Engines (PSEs) to get users up and running quickly. Key features of Swirl include Microsoft 365 integration, SearchProvider configurations, query adaptation, synchronous or asynchronous search federation, optional subscribe feature, pipelining of Processor stages, results stored in SQLite3 or PostgreSQL, built-in Query Transformation support, matching on word stems and handling of stopwords, duplicate detection, re-ranking of unified results using Cosine Vector Similarity, result mixers, page through all results requested, sample data sets, optional spell correction, optional search/result expiration service, easily extensible Connector and Mixer objects, and a welcoming community for collaboration and support.

The Google Cloud Document AI Samples repository contains code samples and Community Samples demonstrating how to analyze, classify, and search documents using Google Cloud Document AI. It includes various projects showcasing different functionalities such as integrating with Google Drive, processing documents using Python, content moderation with Dialogflow CX, fraud detection, language extraction, paper summarization, tax processing pipeline, and more. The repository also provides access to test document files stored in a publicly-accessible Google Cloud Storage Bucket. Additionally, there are codelabs available for optical character recognition (OCR), form parsing, specialized processors, and managing Document AI processors. Community samples, like the PDF Annotator Sample, are also included. Contributions are welcome, and users can seek help or report issues through the repository's issues page. Please note that this repository is not an officially supported Google product and is intended for demonstrative purposes only.

Venice is a derived data storage platform, providing the following characteristics: 1. High throughput asynchronous ingestion from batch and streaming sources (e.g. Hadoop and Samza). 2. Low latency online reads via remote queries or in-process caching. 3. Active-active replication between regions with CRDT-based conflict resolution. 4. Multi-cluster support within each region with operator-driven cluster assignment. 5. Multi-tenancy, horizontal scalability and elasticity within each cluster. The above makes Venice particularly suitable as the stateful component backing a Feature Store, such as Feathr. AI applications feed the output of their ML training jobs into Venice and then query the data for use during online inference workloads.

This is the official repository for 'Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing'. Magpie is a tool designed to synthesize high-quality instruction data at scale by extracting it directly from an aligned Large Language Models (LLMs). It aims to democratize AI by generating large-scale alignment data and enhancing the transparency of model alignment processes. Magpie has been tested on various model families and can be used to fine-tune models for improved performance on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.

This repository, MMC, focuses on advancing multimodal chart understanding through large-scale instruction tuning. It introduces a dataset supporting various tasks and chart types, a benchmark for evaluating reasoning capabilities over charts, and an assistant achieving state-of-the-art performance on chart QA benchmarks. The repository provides data for chart-text alignment, benchmarking, and instruction tuning, along with existing datasets used in experiments. Additionally, it offers a Gradio demo for the MMCA model.

Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

Awesome AI Tools is a curated list of popular tools and resources for artificial intelligence enthusiasts. It includes a wide range of tools such as machine learning libraries, deep learning frameworks, data visualization tools, and natural language processing resources. Whether you are a beginner or an experienced AI practitioner, this repository aims to provide you with a comprehensive collection of tools to enhance your AI projects and research. Explore the list to discover new tools, stay updated with the latest advancements in AI technology, and find the right resources to support your AI endeavors.

SoM-LLaVA is a new data source and learning paradigm for Multimodal LLMs, empowering open-source Multimodal LLMs with Set-of-Mark prompting and improved visual reasoning ability. The repository provides a new dataset that is complementary to existing training sources, enhancing multimodal LLMs with Set-of-Mark prompting and improved general capacity. By adding 30k SoM data to the visual instruction tuning stage of LLaVA, the tool achieves 1% to 6% relative improvements on all benchmarks. Users can train SoM-LLaVA via command line and utilize the implementation to annotate COCO images with SoM. Additionally, the tool can be loaded in Huggingface for further usage.

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