Best AI tools for< Identify Clauses >

DocAI is an API-driven platform that enables you to implement contracts AI into your applications, without requiring development from the ground-up. Our AI identifies and extracts 1,300+ common legal clauses, provisions and data points from a variety of document types. Our AI is a low-code experience for all. Easily train new fields without the need for a data scientist. All you need is subject matter expertise. Flexible and scalable. Flexible deployment options in the Zuva hosted cloud or on prem, across multiple geographical regions. Reliable, expert-built AI our customers can trust. Over 1,300+ out of the box AI fields that are built and trained by experienced lawyers and subject matter experts. Fields identify and extract common legal clauses, provisions and data points from unstructured documents and contracts, including ones written in non-standard language.

Pincites is an AI contract review tool designed for busy legal teams to streamline the contract review process. It offers automated redlining, suggestions, and trend analysis within Microsoft Word, helping legal professionals negotiate contracts faster and more efficiently. Pincites leverages AI to provide real-time feedback, learn user preferences, and identify patterns in contracts to enhance negotiation strategies.

Loops is an AI tool that empowers data analysts and product managers to make informed decisions based on deep, accurate causal insights. It leverages proprietary causal inference models to identify opportunities for maximizing key performance indicators (KPIs) without the need for traditional A/B testing. By analyzing user behaviors and business metrics, Loops helps companies prioritize efforts efficiently and proactively review impactful opportunities. The tool simplifies the process of understanding causality, providing actionable insights for product teams to drive growth and increase KPIs.

Kaizan is an all-in-one AI platform that helps businesses measure client sentiment, increase client coverage, and optimize productivity. It uses AI to analyze client communication and identify the root causes affecting clients from renewing and scaling. Kaizan also provides AI-generated client development plans that help businesses close more deals and increase revenue.

Wild Moose is an AI-powered SRE Copilot tool designed to help companies handle incidents efficiently. It offers fast and efficient root cause analysis that improves with every incident by automatically gathering and analyzing logs, metrics, and code to pinpoint root causes. The tool converts tribal knowledge into custom playbooks, constantly improves performance with a system model that learns from each incident, and integrates seamlessly with various observability tools and deployment platforms. Wild Moose reduces cognitive load on teams, automates routine tasks, and provides actionable insights in real-time, enabling teams to act fast during outages.

Skillsoft Percipio is an AI-driven online learning platform that helps organizations identify and measure skill proficiencies to ensure their workforce stays relevant. The platform makes skilling personalized and accessible, offering a blend of self-paced online courses, hands-on practice, virtual live online classes, compliance courses, and coaching to close skill gaps and mitigate risk. And it’s available anytime, anywhere, on any device.

DeltaGen is an AI-powered win-loss analysis tool that combines artificial intelligence and human expertise to provide precise and unbiased insights directly from buyers. By leveraging technology, approach, and expertise, DeltaGen helps organizations quickly make informed decisions, drive higher win rates, and unlock growth. The platform offers real-time win-loss analysis at scale, empowering teams to identify strengths and weaknesses, keep a pulse on customers' needs, and improve sales performance.

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Iodine Software is a healthcare technology company that provides AI-enabled solutions for revenue cycle management, clinical documentation integrity, and utilization management. The company's flagship product, AwareCDI, is a suite of solutions that addresses the root causes of mid-cycle revenue leakage from admission through post-billing review. AwareCDI uses Iodine's CognitiveML AI engine to spot what is missing in patient documentation based on clinical evidence. This enables healthcare organizations to maximize documentation integrity and revenue capture. Iodine Software also offers AwareUM, a continuous, intelligent prioritization solution for peak UM performance.

AdminIQ is an AI-powered site reliability platform that helps businesses improve the reliability and performance of their websites and applications. It uses machine learning to analyze data from various sources, including application logs, metrics, and user behavior, to identify and resolve issues before they impact users. AdminIQ also provides a suite of tools to help businesses automate their site reliability processes, such as incident management, change management, and performance monitoring.

FairPlay is a Fairness-as-a-Service solution designed for financial institutions, offering AI-powered tools to assess automated decisioning models quickly. It helps in increasing fairness and profits by optimizing marketing, underwriting, and pricing strategies. The application provides features such as Fairness Optimizer, Second Look, Customer Composition, Redline Status, and Proxy Detection. FairPlay enables users to identify and overcome tradeoffs between performance and disparity, assess geographic fairness, de-bias proxies for protected classes, and tune models to reduce disparities without increasing risk. It offers advantages like increased compliance, speed, and readiness through automation, higher approval rates with no increase in risk, and rigorous Fair Lending analysis for sponsor banks and regulators. However, some disadvantages include the need for data integration, potential bias in AI algorithms, and the requirement for technical expertise to interpret results.

Bugpilot is an error monitoring tool specifically designed for React applications. It offers a comprehensive platform for error tracking, debugging, and user communication. With Bugpilot, developers can easily integrate error tracking into their React applications without any code changes or dependencies. The tool provides a user-friendly dashboard that helps developers quickly identify and prioritize errors, understand their root causes, and plan fixes. Bugpilot also includes features such as AI-assisted debugging, session recordings, and customizable error pages to enhance the user experience and reduce support requests.

RPG AI Selfie is an innovative platform that transforms selfies into captivating RPG characters, offering a variety of fantasy classes for customization. It allows users to create unique gaming avatars with personalized features, outfits, and accessories. The platform fosters community engagement among gamers and content creators, providing a space to share creations and gain inspiration. With a focus on enhancing user experiences, RPG AI Selfie empowers users to unleash their creativity and express their gaming identity through character design.

Font Finder by What Font Is is an AI-powered tool that allows users to identify any font from any image, whether commercial or free. Users can upload an image, and the AI-powered font finder will match it with over 990K+ fonts, including both commercial and free options. The tool then displays more than 60 similar fonts for users to explore and use. Font Finder aims to provide users with a seamless experience in identifying and choosing fonts for various design projects.

Pl@ntNet is a citizen science project available as an application that helps you identify plants from your photos. It is a collaborative project that brings together scientists, naturalists, and citizens from all over the world to collect and share data on plant diversity. The app uses artificial intelligence to identify plants from photos, and the data collected is used to create a global database of plant diversity. Pl@ntNet is free to use and is available in over 20 languages.

Spot is a web application that requires JavaScript to be enabled to run. It is a tool designed for specific purposes, although the detailed description of its functionality is not provided in the text snippet. The application likely offers features related to spotting or identifying certain elements or patterns, but further information is needed to provide a comprehensive overview.

Retorio is a cutting-edge Behavioral Intelligence (BI) Platform that fuses machine learning with scientific findings from psychology and organizational research to ultimately take learning and development to a new level within organizations. At the core of Retorio’s capabilities are its AI-powered immersive video simulations. Through these engaging role-plays, learners using Retorio get to train and develop the necessary skills through realistic scenarios. Furthermore, the personalized, on-demand feedback learners receive allows for immediate behavior change and performance improvement. Retorio’s training platform transcends the limitation of scalability and redefines how individuals and teams train and develop, bringing talent development to a new dimension.

Siwalu is an AI-based image recognition application that specializes in identifying animals. The app helps pet owners learn more about their pets by providing specific information about their breed and characteristics. It offers a quick and reliable way to determine the breed of dogs, cats, and horses, including mixed breeds, without the need for costly DNA analysis. Siwalu aims to increase knowledge about global biodiversity by developing a universal animal recognition system.

Signum.AI is a sales intelligence platform that uses artificial intelligence (AI) to help businesses identify customers who are ready to buy. The platform tracks key customer behaviors, such as social media engagement, job changes, product launches, and keyword mentions, to identify the best time to reach out to them. Signum.AI also provides personalized recommendations on how to approach each customer, based on their individual needs and interests.

Dog Identifier is an AI-based application that helps users identify over 170+ dog breeds by simply providing an image or video of a dog. The app predicts the breed of the dog and provides detailed information about characteristics, temperament, and history of the breed. Users can also search for their ideal furry companion by answering a few lifestyle-related questions. Additionally, the app features a comprehensive database of dog breeds, daily fun facts, and a new Dog Mood Detection feature that analyzes a dog's facial expressions and body language to suggest their mood.

The 'Awesome AI Guidelines' repository aims to simplify the ecosystem of guidelines, principles, codes of ethics, standards, and regulations around artificial intelligence. It provides a comprehensive collection of resources addressing ethical and societal challenges in AI systems, including high-level frameworks, principles, processes, checklists, interactive tools, industry standards initiatives, online courses, research, and industry newsletters, as well as regulations and policies from various countries. The repository serves as a valuable reference for individuals and teams designing, building, and operating AI systems to navigate the complex landscape of AI ethics and governance.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Awesome Responsible AI is a curated list of academic research, books, code of ethics, courses, data sets, frameworks, institutes, newsletters, principles, podcasts, reports, tools, regulations, and standards related to Responsible, Trustworthy, and Human-Centered AI. It covers various concepts such as Responsible AI, Trustworthy AI, Human-Centered AI, Responsible AI frameworks, AI Governance, and more. The repository provides a comprehensive collection of resources for individuals interested in ethical, transparent, and accountable AI development and deployment.

Tools4AI is a Java-based Agentic Framework for building AI agents to integrate with enterprise Java applications. It enables the conversion of natural language prompts into actionable behaviors, streamlining user interactions with complex systems. By leveraging AI capabilities, it enhances productivity and innovation across diverse applications. The framework allows for seamless integration of AI with various systems, such as customer service applications, to interpret user requests, trigger actions, and streamline workflows. Prompt prediction anticipates user actions based on input prompts, enhancing user experience by proactively suggesting relevant actions or services based on context.

HolmesGPT is an open-source DevOps assistant powered by OpenAI or any tool-calling LLM of your choice. It helps in troubleshooting Kubernetes, incident response, ticket management, automated investigation, and runbook automation in plain English. The tool connects to existing observability data, is compliance-friendly, provides transparent results, supports extensible data sources, runbook automation, and integrates with existing workflows. Users can install HolmesGPT using Brew, prebuilt Docker container, Python Poetry, or Docker. The tool requires an API key for functioning and supports OpenAI, Azure AI, and self-hosted LLMs.

SinkFinder is a tool designed to analyze jar and zip files for security vulnerabilities. It allows users to define rules for white and blacklisting specific classes and methods that may pose a risk. The tool provides a list of common security sink names along with severity levels and associated vulnerable methods. Users can use SinkFinder to quickly identify potential security issues in their Java applications by scanning for known sink patterns and configurations.

Agentless is an open-source tool designed for automatically solving software development problems. It follows a two-phase process of localization and repair to identify faults in specific files, classes, and functions, and generate candidate patches for fixing issues. The tool is aimed at simplifying the software development process by automating issue resolution and patch generation.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Xata Agent is an open source tool designed to monitor PostgreSQL databases, identify issues, and provide recommendations for improvements. It acts as an AI expert, offering proactive suggestions for configuration tuning, troubleshooting performance issues, and common database problems. The tool is extensible, supports monitoring from cloud services like RDS & Aurora, and uses preset SQL commands to ensure database safety. Xata Agent can run troubleshooting statements, notify users of issues via Slack, and supports multiple AI models for enhanced functionality. It is actively used by the Xata team to manage Postgres databases efficiently.

The Radicalbit AI Monitoring Platform provides a comprehensive solution for monitoring Machine Learning and Large Language models in production. It helps proactively identify and address potential performance issues by analyzing data quality, model quality, and model drift. The repository contains files and projects for running the platform, including UI, API, SDK, and Spark components. Installation using Docker compose is provided, allowing deployment with a K3s cluster and interaction with a k9s container. The platform documentation includes a step-by-step guide for installation and creating dashboards. Community engagement is encouraged through a Discord server. The roadmap includes adding functionalities for batch and real-time workloads, covering various model types and tasks.

Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

Brokk is a code assistant designed to understand code semantically, allowing LLMs to work effectively on large codebases. It offers features like agentic search, summarizing related classes, parsing stack traces, adding source for usages, and autonomously fixing errors. Users can interact with Brokk through different panels and commands, enabling them to manipulate context, ask questions, search codebase, run shell commands, and more. Brokk helps with tasks like debugging regressions, exploring codebase, AI-powered refactoring, and working with dependencies. It is particularly useful for making complex, multi-file edits with o1pro.

Gemini-Android is a mobile application that allows users to track their expenses and manage their finances on the go. The app provides a user-friendly interface for adding and categorizing expenses, setting budgets, and generating reports to help users make informed financial decisions. With Gemini-Android, users can easily monitor their spending habits, identify areas for saving, and stay on top of their financial goals.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

SpeciesNet is an ensemble of AI models designed for classifying wildlife in camera trap images. It consists of an object detector that finds objects of interest in wildlife camera images and an image classifier that classifies those objects to the species level. The ensemble combines these two models using heuristics and geographic information to assign each image to a single category. The models have been trained on a large dataset of camera trap images and are used for species recognition in the Wildlife Insights platform.

Detoxify is a library that provides trained models and code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. It includes models like 'original', 'unbiased', and 'multilingual' trained on different datasets to detect toxicity and minimize bias. The library aims to help in stopping harmful content online by interpreting visual content in context. Users can fine-tune the models on carefully constructed datasets for research purposes or to aid content moderators in flagging out harmful content quicker. The library is built to be user-friendly and straightforward to use.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

This GPT matches between system or application to it's relevant clauses in military standards. You can simply ask any question or state your system

Focuses on investigative questioning in the Five Whys process.

Guide for root cause analysis using fishbone diagrams, encouraging detailed problem-solving.

Critically analyze SLIs/SLOs with a deeper, provocative approach

Informative guide on dehydration, its causes, symptoms, and prevention.

Provides info on skin rashes, their causes, types, and remedies in an informative tone.

「痛みを経験する前に考える」または「痛みを経験してから考える」を分析する

Just send us an image of a scene from a video work and i will guess the name of the work!

Identify and explain mental models for various scenarios.

Analyzes images to identify landmarks and shares historical insights and captivating facts.

Identify and cherish your vintage

Food diary to help you identify food intolerance

Identify and clarify personal values to cultivate a strong sense of purpose and self-confidence

Identify key claims and sniff past the BS with your personal AI Logic Checker and Fallacy Expert.

I confidently identify plants from photos, diagnose issues, and offer advice.

Enables sales & marketing leadership to identify high-value AI use cases

I identify various rocks from images and advise consulting a geologist for certainty.

This interactive inquiry will help identify your relationship attachment style.

Identify your sources of fear and anger and convert those emotions into concrete next steps. Tested and approved by the real Matt Mochary!

Identify the best SaaS sales organizations. Click on the prompt to receive a full report that includes: G2, Glassdoor, and Repvue reviews.