Best AI tools for< Improve Data Accuracy >

Datuum is an AI-powered data onboarding solution that offers seamless integration for businesses. It simplifies the data onboarding process by automating manual tasks, generating code, and ensuring data accuracy with AI-driven validation. Datuum helps businesses achieve faster time to value, reduce costs, improve scalability, and enhance data quality and consistency.

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.

Eigen Technologies is an AI-powered data extraction platform designed for business users to automate the extraction of data from various documents. The platform offers solutions for intelligent document processing and automation, enabling users to streamline business processes, make informed decisions, and achieve significant efficiency gains. Eigen's platform is purpose-built to deliver real ROI by reducing manual processes, improving data accuracy, and accelerating decision-making across industries such as corporates, banks, financial services, insurance, law, and manufacturing. With features like generative insights, table extraction, pre-processing hub, and model governance, Eigen empowers users to automate data extraction workflows efficiently. The platform is known for its unmatched accuracy, speed, and capability, providing customers with a flexible and scalable solution that integrates seamlessly with existing systems.

Swyft AI is an AI-powered tool that automates CRM data capture and sales motions. It integrates with popular CRM and web conferencing tools, allowing sales teams to save time and improve data accuracy. Swyft AI's key features include automatic CRM data capture, workflow automation, and call summarization. It offers advantages such as improved data hygiene, increased sales productivity, and reduced manual work for revenue teams.

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.

CommodityAI is a web-based platform that uses AI, automation, and collaboration tools to help businesses manage their commodity shipments and supply chains more efficiently. The platform offers a range of features, including shipment management automation, intelligent document processing, stakeholder collaboration, and supply-chain automation. CommodityAI can help businesses improve data accuracy, eliminate manual processes, and streamline communication and collaboration. The platform is designed for the commodities industry and offers commodity-specific automations, ERP integration, and AI-powered insights.

People.ai is an AI-powered platform that revolutionizes the way businesses approach revenue generation. By leveraging generative AI, automated activity capture, and data insights, People.ai helps sales teams optimize their GTM (Go-To-Market) strategy, improve customer interactions, and scale revenue-generating activities. The platform offers a holistic solution that combines automation, data accuracy, and AI-driven recommendations to enhance sales performance and drive business growth.

Canoe is a cloud-based platform that leverages machine learning technology to automate document collection, data extraction, and data science initiatives for alternative investments. It transforms complex documents into actionable intelligence within seconds, empowering allocators with tools to unlock new efficiencies for their business. Canoe is trusted by thousands of alternative investors, allocators, wealth management, and asset servicers to improve efficiency, accuracy, and completeness of investment data.

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.

Cape is an AI tool designed to enhance sales, customer service, marketing, KYC, and risk management processes. It offers agentic workflow automation, personalized marketing solutions, and AI-powered knowledge retrieval. Cape enables users to build custom applications using its API and chat interface for easy access to LLMs. The tool aims to improve productivity, operational efficiency, and data accuracy across various business functions.

Claravine is an AI tool that creates shared language and standards for marketing data. It eliminates flawed data, conflict, and the blame game by ensuring data accuracy, improving accountability, and building trust. The platform helps enterprise brands and agencies deliver on the promise of modern marketing by consistently creating and applying data standards. With features like defining standards, applying standards, and connecting standards, Claravine maximizes campaign and technology investments to drive better results. Customers report significant improvements in return on ad spend, data compliance, and time spent on manual data processes. The platform enables global collaboration, automates data flows, and adapts quickly to changing business needs. Trusted by global brands, Claravine provides visibility into campaign performance, data consistency across organizations, and a single source of truth for marketing data.

AmyGB Platform Services offers Gen AI-powered Document Processing and API Services to supercharge productivity for businesses. Their trendsetting digital products have revolutionized how organizations handle data and streamline workflows, enabling businesses to easily optimize operations 24x7, enhance data accuracy, and improve customer satisfaction. The platform empowers business operations by driving automation revolution, providing 8x productivity, 70% cost efficiency, 80% higher accuracy, and 95% automation. AmyGB's AI-powered document processing solutions help convert documents into digital assets, extract data, and enhance customer fulfillment through automated software solutions.

Automaited is an AI application that offers Ada - an AI Agent for automating order processing. Ada handles orders from receipt to ERP entry, extracting, validating, and transferring data to ensure accuracy and efficiency. The application utilizes state-of-the-art AI technology to streamline order processing, saving time, reducing errors, and enabling users to focus on customer satisfaction. With seamless automation, Ada integrates into ERP systems, making order processing effortless, quick, and cost-efficient. Automaited provides tailored automations to make operational processes up to 70% more efficient, enhancing performance and reducing error rates.

Snaplet is a data management tool for developers that provides AI-generated dummy data for local development, end-to-end testing, and debugging. It uses a real programming language (TypeScript) to define and edit data, ensuring type safety and auto-completion. Snaplet understands database structures and relationships, automatically transforming personally identifiable information and seeding data accordingly. It integrates seamlessly into development workflows, providing data where it's needed most: on local machines, for CI/CD testing, and preview environments.

Sherpa.ai is a SaaS platform that enables data collaborations without sharing data. It allows businesses to build and train models with sensitive data from different parties, without compromising privacy or regulatory compliance. Sherpa.ai's Federated Learning platform is used in various industries, including healthcare, financial services, and manufacturing, to improve AI models, accelerate research, and optimize operations.

Fine-Tune AI is a tool that allows users to generate fine-tune data sets using prompts. This can be useful for a variety of tasks, such as improving the accuracy of machine learning models or creating new training data for AI applications.

Kira Systems is a machine learning contract search, review, and analysis software that helps businesses identify, extract, and analyze content in their contracts and documents. It uses patented machine learning technology to extract concepts and data points with high efficiency and accuracy. Kira also has built-in intelligence that streamlines the contract review process with out-of-the-box smart fields. Businesses can also create their own smart fields to find specific data points using Kira's no-code machine learning tool. Kira's adaptive workflows allow businesses to organize, track, and export results. Kira has a partner ecosystem that allows businesses to transform how teams work with their contracts.

The website offers a precision workflow solution for enterprises in the finance industry, combining AI technology with human oversight to empower financial decisions. It provides features such as accurate search citations, multilingual models, and complex human-in-loop automation. The application integrates seamlessly with existing platforms, uses advanced AI models, and offers meaningful time savings. Users can benefit from the application's ability to ingest unstructured data, improve over time, and avoid hallucinations.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

Lexset is an AI tool that provides synthetic data generation services for computer vision model training. It offers a no-code interface to create unlimited data with advanced camera controls and lighting options. Users can simulate AI-scale environments, composite objects into images, and create custom 3D scenarios. Lexset also provides access to GPU nodes, dedicated support, and feature development assistance. The tool aims to improve object detection accuracy and optimize generalization on high-quality synthetic data.

TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

This repository focuses on utilizing generative AI to bridge the gap between natural language questions and SQL queries, aiming to improve data consumption in enterprise data warehouses. It addresses challenges in SQL query generation, such as foreign key relationships and table joins, and highlights the importance of accuracy metrics like Execution Accuracy (EX) and Exact Set Match Accuracy (EM). The workshop content covers advanced prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning models, and security measures against prompt and SQL injections.

ProX is a lm-based data refinement framework that automates the process of cleaning and improving data used in pre-training large language models. It offers better performance, domain flexibility, efficiency, and cost-effectiveness compared to traditional methods. The framework has been shown to improve model performance by over 2% and boost accuracy by up to 20% in tasks like math. ProX is designed to refine data at scale without the need for manual adjustments, making it a valuable tool for data preprocessing in natural language processing tasks.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

LLMs is a Chinese large language model technology stack for practical use. It includes high-availability pre-training, SFT, and DPO preference alignment code framework. The repository covers pre-training data cleaning, high-concurrency framework, SFT dataset cleaning, data quality improvement, and security alignment work for Chinese large language models. It also provides open-source SFT dataset construction, pre-training from scratch, and various tools and frameworks for data cleaning, quality optimization, and task alignment.

xFinder is a model specifically designed for key answer extraction from large language models (LLMs). It addresses the challenges of unreliable evaluation methods by optimizing the key answer extraction module. The model achieves high accuracy and robustness compared to existing frameworks, enhancing the reliability of LLM evaluation. It includes a specialized dataset, the Key Answer Finder (KAF) dataset, for effective training and evaluation. xFinder is suitable for researchers and developers working with LLMs to improve answer extraction accuracy.

xGitGuard is an AI-based system developed by Comcast Cybersecurity Research and Development team to detect secrets (e.g., API tokens, usernames, passwords) exposed on GitHub repositories. It uses advanced Natural Language Processing to detect secrets at scale and with appropriate velocity. The tool provides workflows for detecting credentials and keys/tokens in both enterprise and public GitHub accounts. Users can set up search patterns, configure API access, run detections with or without ML filters, and train ML models for improved detection accuracy. xGitGuard also supports custom keyword scans for targeted organizations or repositories. The tool is licensed under Apache 2.0.

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

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.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

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.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Yuna AI is a unique AI companion designed to form a genuine connection with users. It runs exclusively on the local machine, ensuring privacy and security. The project offers features like text generation, language translation, creative content writing, roleplaying, and informal question answering. The repository provides comprehensive setup and usage guides for Yuna AI, along with additional resources and tools to enhance the user experience.

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

A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure. Fine-Tuning retrains an existing pre-trained LLM using example data, resulting in a new 'custom' fine-tuned LLM optimized for task-specific examples. Use cases include improving LLM performance on specific tasks and introducing information not well represented by the base LLM model. Suitable for cases where latency is critical, high accuracy is required, and clear evaluation metrics are available. Learning path includes labs for fine-tuning GPT and Llama2 models via Dashboards and Python SDK.

I check code samples for common mistakes and accuracy

I will create a quality data set for a personality AI, just dive into each module by saying the name of it and do so for all the modules. If you find it useful, share it to your friends

根据用户输入的数据参数生成标签

I'm Back Propagation, here to help you understand and apply back propagation techniques to your AI models.

Expert in diagnosing and fixing GPT issues.

Expert in refining custom GPT model instructions.

CUDA and Triton expert with concise, accurate answers.

Esperto in scommesse calcistiche con ML 2.0

WRC 번역 데이터베이스 참조, 정확도 주의.

Language and Skill Training Centers collect personal information of learners, including progress tracking and sometimes payment details.

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

Turn your projects' Ideas into Clear Flowcharts(data flow) with Recommended Tech Stack

Fix mismatched entries in your data

Get into FAANG. Practice with an AI expert in algorithms, data structures, and system design. Do a mock interview and improve.

Moved to https://chat.openai.com/g/g-KFnF7qssT-palm-reader . Interprets palm readings from user-uploaded hand images. Turned off setting to use data for OpenAi to improve model.

Moved to https://chat.openai.com/g/g-q6GNcOkYx-face-reader. Reads faces to tell fortunes based on Chinese face reading. Turned off setting to use data for OpenAi to improve model.

Designing Strategic M&A Blueprints for Success in buying, selling or merging companies. Use this GPT to simplify, speed up and improve the quality of the M&A process. With custom data - 100s of creative options in deal flow, deal structuring, financing and more. **Version 2.2 - 28012024**

メールの作成・添削・返信の提案などを行います。メールの校正は、草案をそのままコピペするだけで可能です。件名・署名・添付ファイルなどを忘れないようリマインドも行います。さらにこのGPTは、特別な設定で「Use conversation data in your GPT to improve our models」の項目をOFFにしています。そのため、うっかり機密情報が含まれるメール文を送信してしまった場合でも、ChatGPTの学習に利用されないと思われます。 ※ただし、今後の仕様変更や他の経路からの情報漏洩などのリスクもありえます。個人情報は決して書き込まないでください。

Improve your curriculum telling chatgpt your cv and the desired job

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